Inference

Sampling

Functions for MCMC sampling.

pymc3.sampling.fast_sample_posterior_predictive(trace: MultiTrace | Dataset | InferenceData | list[dict[str, np.ndarray]], samples: int | None = None, model: Model | None = None, var_names: list[str] | None = None, keep_size: bool = False, random_seed=None) dict[str, np.ndarray]

Generate posterior predictive samples from a model given a trace.

This is a vectorized alternative to the standard sample_posterior_predictive function. It aims to be as compatible as possible with the original API, and is significantly faster. Both posterior predictive sampling functions have some remaining issues, and we encourage users to verify agreement across the results of both functions for the time being.

Parameters
trace: MultiTrace, xarray.Dataset, InferenceData, or List of points (dictionary)

Trace generated from MCMC sampling.

samples: int, optional

Number of posterior predictive samples to generate. Defaults to one posterior predictive sample per posterior sample, that is, the number of draws times the number of chains. It is not recommended to modify this value; when modified, some chains may not be represented in the posterior predictive sample.

model: Model (optional if in `with` context)

Model used to generate trace

var_names: Iterable[str]

List of vars to sample.

keep_size: bool, optional

Force posterior predictive sample to have the same shape as posterior and sample stats data: (nchains, ndraws, ...).

random_seed: int

Seed for the random number generator.

Returns
samples: dict

Dictionary with the variable names as keys, and values numpy arrays containing posterior predictive samples.

pymc3.sampling.init_nuts(init='auto', chains=1, n_init=500000, model=None, random_seed=None, progressbar=True, jitter_max_retries=10, **kwargs)

Set up the mass matrix initialization for NUTS.

NUTS convergence and sampling speed is extremely dependent on the choice of mass/scaling matrix. This function implements different methods for choosing or adapting the mass matrix.

Parameters
initstr

Initialization method to use.

  • auto: Choose a default initialization method automatically. Currently, this is jitter+adapt_diag, but this can change in the future. If you depend on the exact behaviour, choose an initialization method explicitly.

  • adapt_diag: Start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. All chains use the test value (usually the prior mean) as starting point.

  • jitter+adapt_diag: Same as adapt_diag, but use test value plus a uniform jitter in [-1, 1] as starting point in each chain.

  • advi+adapt_diag: Run ADVI and then adapt the resulting diagonal mass matrix based on the sample variance of the tuning samples.

  • advi+adapt_diag_grad: Run ADVI and then adapt the resulting diagonal mass matrix based on the variance of the gradients during tuning. This is experimental and might be removed in a future release.

  • advi: Run ADVI to estimate posterior mean and diagonal mass matrix.

  • advi_map: Initialize ADVI with MAP and use MAP as starting point.

  • map: Use the MAP as starting point. This is discouraged.

  • adapt_full: Adapt a dense mass matrix using the sample covariances. All chains use the test value (usually the prior mean) as starting point.

  • jitter+adapt_full: Same as adapt_full, but use test value plus a uniform jitter in [-1, 1] as starting point in each chain.

chainsint

Number of jobs to start.

n_initint

Number of iterations of initializer. Only works for ‘ADVI’ init methods.

modelModel (optional if in with context)
progressbarbool

Whether or not to display a progressbar for advi sampling.

jitter_max_retriesint

Maximum number of repeated attempts (per chain) at creating an initial matrix with uniform jitter that yields a finite probability. This applies to jitter+adapt_diag and jitter+adapt_full init methods.

**kwargskeyword arguments

Extra keyword arguments are forwarded to pymc3.NUTS.

Returns
startpymc3.model.Point

Starting point for sampler

nuts_samplerpymc3.step_methods.NUTS

Instantiated and initialized NUTS sampler object

pymc3.sampling.iter_sample(draws: int, step, start: Optional[Dict[Any, Any]] = None, trace=None, chain=0, tune: Optional[int] = None, model: Optional[pymc3.model.Model] = None, random_seed: Optional[Union[int, List[int]]] = None, callback=None)

Generate a trace on each iteration using the given step method.

Multiple step methods ared supported via compound step methods. Returns the amount of time taken.

Parameters
drawsint

The number of samples to draw

stepfunction

Step function

startdict

Starting point in parameter space (or partial point). Defaults to trace.point(-1)) if there is a trace provided and model.test_point if not (defaults to empty dict)

tracebackend, list, or MultiTrace

This should be a backend instance, a list of variables to track, or a MultiTrace object with past values. If a MultiTrace object is given, it must contain samples for the chain number chain. If None or a list of variables, the NDArray backend is used.

chainint, optional

Chain number used to store sample in backend. If cores is greater than one, chain numbers will start here.

tuneint, optional

Number of iterations to tune, if applicable (defaults to None)

modelModel (optional if in with context)
random_seedint or list of ints, optional

A list is accepted if more if cores is greater than one.

callback :

A function which gets called for every sample from the trace of a chain. The function is called with the trace and the current draw and will contain all samples for a single trace. the draw.chain argument can be used to determine which of the active chains the sample is drawn from. Sampling can be interrupted by throwing a KeyboardInterrupt in the callback.

Yields
traceMultiTrace

Contains all samples up to the current iteration

Examples

for trace in iter_sample(500, step):
    ...
pymc3.sampling.sample(draws=1000, step=None, init='auto', n_init=200000, start=None, trace=None, chain_idx=0, chains=None, cores=None, tune=1000, progressbar=True, model=None, random_seed=None, discard_tuned_samples=True, compute_convergence_checks=True, callback=None, jitter_max_retries=10, *, return_inferencedata=None, idata_kwargs: Optional[dict] = None, mp_ctx=None, pickle_backend: str = 'pickle', **kwargs)

Draw samples from the posterior using the given step methods.

Multiple step methods are supported via compound step methods.

Parameters
drawsint

The number of samples to draw. Defaults to 1000. The number of tuned samples are discarded by default. See discard_tuned_samples.

initstr

Initialization method to use for auto-assigned NUTS samplers.

  • auto: Choose a default initialization method automatically. Currently, this is jitter+adapt_diag, but this can change in the future. If you depend on the exact behaviour, choose an initialization method explicitly.

  • adapt_diag: Start with a identity mass matrix and then adapt a diagonal based on the variance of the tuning samples. All chains use the test value (usually the prior mean) as starting point.

  • jitter+adapt_diag: Same as adapt_diag, but add uniform jitter in [-1, 1] to the starting point in each chain.

  • advi+adapt_diag: Run ADVI and then adapt the resulting diagonal mass matrix based on the sample variance of the tuning samples.

  • advi+adapt_diag_grad: Run ADVI and then adapt the resulting diagonal mass matrix based on the variance of the gradients during tuning. This is experimental and might be removed in a future release.

  • advi: Run ADVI to estimate posterior mean and diagonal mass matrix.

  • advi_map: Initialize ADVI with MAP and use MAP as starting point.

  • map: Use the MAP as starting point. This is discouraged.

  • adapt_full: Adapt a dense mass matrix using the sample covariances

stepfunction or iterable of functions

A step function or collection of functions. If there are variables without step methods, step methods for those variables will be assigned automatically. By default the NUTS step method will be used, if appropriate to the model; this is a good default for beginning users.

n_initint

Number of iterations of initializer. Only works for ‘ADVI’ init methods.

startdict, or array of dict

Starting point in parameter space (or partial point) Defaults to trace.point(-1)) if there is a trace provided and model.test_point if not (defaults to empty dict). Initialization methods for NUTS (see init keyword) can overwrite the default.

tracebackend, list, or MultiTrace

This should be a backend instance, a list of variables to track, or a MultiTrace object with past values. If a MultiTrace object is given, it must contain samples for the chain number chain. If None or a list of variables, the NDArray backend is used.

chain_idxint

Chain number used to store sample in backend. If chains is greater than one, chain numbers will start here.

chainsint

The number of chains to sample. Running independent chains is important for some convergence statistics and can also reveal multiple modes in the posterior. If None, then set to either cores or 2, whichever is larger.

coresint

The number of chains to run in parallel. If None, set to the number of CPUs in the system, but at most 4.

tuneint

Number of iterations to tune, defaults to 1000. Samplers adjust the step sizes, scalings or similar during tuning. Tuning samples will be drawn in addition to the number specified in the draws argument, and will be discarded unless discard_tuned_samples is set to False.

progressbarbool, optional default=True

Whether or not to display a progress bar in the command line. The bar shows the percentage of completion, the sampling speed in samples per second (SPS), and the estimated remaining time until completion (“expected time of arrival”; ETA).

modelModel (optional if in with context)
random_seedint or list of ints

A list is accepted if cores is greater than one.

discard_tuned_samplesbool

Whether to discard posterior samples of the tune interval.

compute_convergence_checksbool, default=True

Whether to compute sampler statistics like Gelman-Rubin and effective_n.

callbackfunction, default=None

A function which gets called for every sample from the trace of a chain. The function is called with the trace and the current draw and will contain all samples for a single trace. the draw.chain argument can be used to determine which of the active chains the sample is drawn from. Sampling can be interrupted by throwing a KeyboardInterrupt in the callback.

jitter_max_retriesint

Maximum number of repeated attempts (per chain) at creating an initial matrix with uniform jitter that yields a finite probability. This applies to jitter+adapt_diag and jitter+adapt_full init methods.

return_inferencedatabool, default=False

Whether to return the trace as an arviz.InferenceData (True) object or a MultiTrace (False) Defaults to False, but we’ll switch to True in an upcoming release.

idata_kwargsdict, optional

Keyword arguments for arviz.from_pymc3()

mp_ctxmultiprocessing.context.BaseContent

A multiprocessing context for parallel sampling. See multiprocessing documentation for details.

pickle_backendstr

One of ‘pickle’ or ‘dill’. The library used to pickle models in parallel sampling if the multiprocessing context is not of type fork.

Returns
tracepymc3.backends.base.MultiTrace or arviz.InferenceData

A MultiTrace or ArviZ InferenceData object that contains the samples.

Notes

Optional keyword arguments can be passed to sample to be delivered to the step_methods used during sampling.

If your model uses only one step method, you can address step method kwargs directly. In particular, the NUTS step method has several options including:

  • target_accept : float in [0, 1]. The step size is tuned such that we approximate this acceptance rate. Higher values like 0.9 or 0.95 often work better for problematic posteriors

  • max_treedepth : The maximum depth of the trajectory tree

  • step_scale : float, default 0.25 The initial guess for the step size scaled down by \(1/n**(1/4)\)

If your model uses multiple step methods, aka a Compound Step, then you have two ways to address arguments to each step method:

  1. If you let sample() automatically assign the step_methods, and you can correctly anticipate what they will be, then you can wrap step method kwargs in a dict and pass that to sample() with a kwarg set to the name of the step method. e.g. for a CompoundStep comprising NUTS and BinaryGibbsMetropolis, you could send:

    1. target_accept to NUTS: nuts={‘target_accept’:0.9}

    2. transit_p to BinaryGibbsMetropolis: binary_gibbs_metropolis={‘transit_p’:.7}

    Note that available names are:

    nuts, hmc, metropolis, binary_metropolis, binary_gibbs_metropolis, categorical_gibbs_metropolis, DEMetropolis, DEMetropolisZ, slice

  2. If you manually declare the step_methods, within the step kwarg, then you can address the step_method kwargs directly. e.g. for a CompoundStep comprising NUTS and BinaryGibbsMetropolis, you could send

    step=[pm.NUTS([freeRV1, freeRV2], target_accept=0.9),
          pm.BinaryGibbsMetropolis([freeRV3], transit_p=.7)]
    

You can find a full list of arguments in the docstring of the step methods.

Examples

In [1]: import pymc3 as pm
   ...: n = 100
   ...: h = 61
   ...: alpha = 2
   ...: beta = 2

In [2]: with pm.Model() as model: # context management
   ...:     p = pm.Beta("p", alpha=alpha, beta=beta)
   ...:     y = pm.Binomial("y", n=n, p=p, observed=h)
   ...:     trace = pm.sample()

In [3]: az.summary(trace, kind="stats")

Out[3]:
    mean     sd  hdi_3%  hdi_97%
p  0.609  0.047   0.528    0.699
pymc3.sampling.sample_posterior_predictive(trace, samples: Optional[int] = None, model: Optional[pymc3.model.Model] = None, var_names: Optional[List[str]] = None, size: Optional[int] = None, keep_size: Optional[bool] = False, random_seed=None, progressbar: bool = True) Dict[str, numpy.ndarray]

Generate posterior predictive samples from a model given a trace.

Parameters
tracebackend, list, xarray.Dataset, arviz.InferenceData, or MultiTrace

Trace generated from MCMC sampling, or a list of dicts (eg. points or from find_MAP()), or xarray.Dataset (eg. InferenceData.posterior or InferenceData.prior)

samplesint

Number of posterior predictive samples to generate. Defaults to one posterior predictive sample per posterior sample, that is, the number of draws times the number of chains. It is not recommended to modify this value; when modified, some chains may not be represented in the posterior predictive sample.

modelModel (optional if in with context)

Model used to generate trace

varsiterable

Variables for which to compute the posterior predictive samples. Deprecated: please use var_names instead.

var_namesIterable[str]

Names of variables for which to compute the posterior predictive samples.

sizeint

The number of random draws from the distribution specified by the parameters in each sample of the trace. Not recommended unless more than ndraws times nchains posterior predictive samples are needed.

keep_sizebool, optional

Force posterior predictive sample to have the same shape as posterior and sample stats data: (nchains, ndraws, ...). Overrides samples and size parameters.

random_seedint

Seed for the random number generator.

progressbarbool

Whether or not to display a progress bar in the command line. The bar shows the percentage of completion, the sampling speed in samples per second (SPS), and the estimated remaining time until completion (“expected time of arrival”; ETA).

Returns
samplesdict

Dictionary with the variable names as keys, and values numpy arrays containing posterior predictive samples.

pymc3.sampling.sample_posterior_predictive_w(traces, samples: Optional[int] = None, models: Optional[List[pymc3.model.Model]] = None, weights: Optional[Union[numpy.ndarray, List[float]]] = None, random_seed: Optional[int] = None, progressbar: bool = True)

Generate weighted posterior predictive samples from a list of models and a list of traces according to a set of weights.

Parameters
traceslist or list of lists

List of traces generated from MCMC sampling (xarray.Dataset, arviz.InferenceData, or MultiTrace), or a list of list containing dicts from find_MAP() or points. The number of traces should be equal to the number of weights.

samplesint, optional

Number of posterior predictive samples to generate. Defaults to the length of the shorter trace in traces.

modelslist of Model

List of models used to generate the list of traces. The number of models should be equal to the number of weights and the number of observed RVs should be the same for all models. By default a single model will be inferred from with context, in this case results will only be meaningful if all models share the same distributions for the observed RVs.

weightsarray-like, optional

Individual weights for each trace. Default, same weight for each model.

random_seedint, optional

Seed for the random number generator.

progressbarbool, optional default True

Whether or not to display a progress bar in the command line. The bar shows the percentage of completion, the sampling speed in samples per second (SPS), and the estimated remaining time until completion (“expected time of arrival”; ETA).

Returns
samplesdict

Dictionary with the variables as keys. The values corresponding to the posterior predictive samples from the weighted models.

pymc3.sampling.sample_prior_predictive(samples=500, model: Optional[pymc3.model.Model] = None, var_names: Optional[Iterable[str]] = None, random_seed=None) Dict[str, numpy.ndarray]

Generate samples from the prior predictive distribution.

Parameters
samplesint

Number of samples from the prior predictive to generate. Defaults to 500.

modelModel (optional if in with context)
var_namesIterable[str]

A list of names of variables for which to compute the posterior predictive samples. Defaults to both observed and unobserved RVs.

random_seedint

Seed for the random number generator.

Returns
dict

Dictionary with variable names as keys. The values are numpy arrays of prior samples.

Step-methods

pymc3.sampling.assign_step_methods(model, step=None, methods=(<class 'pymc3.step_methods.hmc.nuts.NUTS'>, <class 'pymc3.step_methods.hmc.hmc.HamiltonianMC'>, <class 'pymc3.step_methods.metropolis.Metropolis'>, <class 'pymc3.step_methods.metropolis.BinaryMetropolis'>, <class 'pymc3.step_methods.metropolis.BinaryGibbsMetropolis'>, <class 'pymc3.step_methods.slicer.Slice'>, <class 'pymc3.step_methods.metropolis.CategoricalGibbsMetropolis'>, <class 'pymc3.step_methods.pgbart.PGBART'>), step_kwargs=None)

Assign model variables to appropriate step methods.

Passing a specified model will auto-assign its constituent stochastic variables to step methods based on the characteristics of the variables. This function is intended to be called automatically from sample(), but may be called manually. Each step method passed should have a competence() method that returns an ordinal competence value corresponding to the variable passed to it. This value quantifies the appropriateness of the step method for sampling the variable.

Parameters
modelModel object

A fully-specified model object

stepstep function or vector of step functions

One or more step functions that have been assigned to some subset of the model’s parameters. Defaults to None (no assigned variables).

methodsvector of step method classes

The set of step methods from which the function may choose. Defaults to the main step methods provided by PyMC3.

step_kwargsdict

Parameters for the samplers. Keys are the lower case names of the step method, values a dict of arguments.

Returns
methodslist

List of step methods associated with the model’s variables.

NUTS

class pymc3.step_methods.hmc.nuts.NUTS(*args, **kwargs)

A sampler for continuous variables based on Hamiltonian mechanics.

NUTS automatically tunes the step size and the number of steps per sample. A detailed description can be found at [1], “Algorithm 6: Efficient No-U-Turn Sampler with Dual Averaging”.

NUTS provides a number of statistics that can be accessed with trace.get_sampler_stats:

  • mean_tree_accept: The mean acceptance probability for the tree that generated this sample. The mean of these values across all samples but the burn-in should be approximately target_accept (the default for this is 0.8).

  • diverging: Whether the trajectory for this sample diverged. If there are any divergences after burnin, this indicates that the results might not be reliable. Reparametrization can often help, but you can also try to increase target_accept to something like 0.9 or 0.95.

  • energy: The energy at the point in phase-space where the sample was accepted. This can be used to identify posteriors with problematically long tails. See below for an example.

  • energy_change: The difference in energy between the start and the end of the trajectory. For a perfect integrator this would always be zero.

  • max_energy_change: The maximum difference in energy along the whole trajectory.

  • depth: The depth of the tree that was used to generate this sample

  • tree_size: The number of leafs of the sampling tree, when the sample was accepted. This is usually a bit less than 2 ** depth. If the tree size is large, the sampler is using a lot of leapfrog steps to find the next sample. This can for example happen if there are strong correlations in the posterior, if the posterior has long tails, if there are regions of high curvature (“funnels”), or if the variance estimates in the mass matrix are inaccurate. Reparametrisation of the model or estimating the posterior variances from past samples might help.

  • tune: This is True, if step size adaptation was turned on when this sample was generated.

  • step_size: The step size used for this sample.

  • step_size_bar: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.

  • model_logp: The model log-likelihood for this sample.

  • process_time_diff: The time it took to draw the sample, as defined by the python standard library time.process_time. This counts all the CPU time, including worker processes in BLAS and OpenMP.

  • perf_counter_diff: The time it took to draw the sample, as defined by the python standard library time.perf_counter (wall time).

  • perf_counter_start: The value of time.perf_counter at the beginning of the computation of the draw.

References

1

Hoffman, Matthew D., & Gelman, Andrew. (2011). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.

Set up the No-U-Turn sampler.

Parameters
vars: list of Theano variables, default all continuous vars
Emax: float, default 1000

Maximum energy change allowed during leapfrog steps. Larger deviations will abort the integration.

target_accept: float, default .8

Adapt the step size such that the average acceptance probability across the trajectories are close to target_accept. Higher values for target_accept lead to smaller step sizes. Setting this to higher values like 0.9 or 0.99 can help with sampling from difficult posteriors. Valid values are between 0 and 1 (exclusive).

step_scale: float, default 0.25

Size of steps to take, automatically scaled down by 1/n**(1/4). If step size adaptation is switched off, the resulting step size is used. If adaptation is enabled, it is used as initial guess.

gamma: float, default .05
k: float, default .75

Parameter for dual averaging for step size adaptation. Values between 0.5 and 1 (exclusive) are admissible. Higher values correspond to slower adaptation.

t0: int, default 10

Parameter for dual averaging. Higher values slow initial adaptation.

adapt_step_size: bool, default=True

Whether step size adaptation should be enabled. If this is disabled, k, t0, gamma and target_accept are ignored.

max_treedepth: int, default=10

The maximum tree depth. Trajectories are stopped when this depth is reached.

early_max_treedepth: int, default=8

The maximum tree depth during the first 200 tuning samples.

scaling: array_like, ndim = {1,2}

The inverse mass, or precision matrix. One dimensional arrays are interpreted as diagonal matrices. If is_cov is set to True, this will be interpreded as the mass or covariance matrix.

is_cov: bool, default=False

Treat the scaling as mass or covariance matrix.

potential: Potential, optional

An object that represents the Hamiltonian with methods velocity, energy, and random methods. It can be specified instead of the scaling matrix.

model: pymc3.Model

The model

kwargs: passed to BaseHMC

Notes

The step size adaptation stops when self.tune is set to False. This is usually achieved by setting the tune parameter if pm.sample to the desired number of tuning steps.

static competence(var, has_grad)

Check how appropriate this class is for sampling a random variable.

Metropolis

class pymc3.step_methods.metropolis.BinaryGibbsMetropolis(*args, **kwargs)

A Metropolis-within-Gibbs step method optimized for binary variables

Parameters
vars: list

List of variables for sampler

order: list or ‘random’

List of integers indicating the Gibbs update order e.g., [0, 2, 1, …]. Default is random

transit_p: float

The diagonal of the transition kernel. A value > .5 gives anticorrelated proposals, which resulting in more efficient antithetical sampling. Default is 0.8

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

static competence(var)

BinaryMetropolis is only suitable for Bernoulli and Categorical variables with k=2.

class pymc3.step_methods.metropolis.BinaryMetropolis(*args, **kwargs)

Metropolis-Hastings optimized for binary variables

Parameters
vars: list

List of variables for sampler

scaling: scalar or array

Initial scale factor for proposal. Defaults to 1.

tune: bool

Flag for tuning. Defaults to True.

tune_interval: int

The frequency of tuning. Defaults to 100 iterations.

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

static competence(var)

BinaryMetropolis is only suitable for binary (bool) and Categorical variables with k=1.

class pymc3.step_methods.metropolis.CategoricalGibbsMetropolis(*args, **kwargs)

A Metropolis-within-Gibbs step method optimized for categorical variables. This step method works for Bernoulli variables as well, but it is not optimized for them, like BinaryGibbsMetropolis is. Step method supports two types of proposals: A uniform proposal and a proportional proposal, which was introduced by Liu in his 1996 technical report “Metropolized Gibbs Sampler: An Improvement”.

static competence(var)

CategoricalGibbsMetropolis is only suitable for Bernoulli and Categorical variables.

class pymc3.step_methods.metropolis.DEMetropolis(*args, **kwargs)

Differential Evolution Metropolis sampling step.

Parameters
lamb: float

Lambda parameter of the DE proposal mechanism. Defaults to 2.38 / sqrt(2 * ndim)

vars: list

List of variables for sampler

S: standard deviation or covariance matrix

Some measure of variance to parameterize proposal distribution

proposal_dist: function

Function that returns zero-mean deviates when parameterized with S (and n). Defaults to Uniform(-S,+S).

scaling: scalar or array

Initial scale factor for epsilon. Defaults to 0.001

tune: str

Which hyperparameter to tune. Defaults to None, but can also be ‘scaling’ or ‘lambda’.

tune_interval: int

The frequency of tuning. Defaults to 100 iterations.

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

mode: string or `Mode` instance.

compilation mode passed to Theano functions

References

Braak2006

Cajo C.F. ter Braak (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing link

Parameters
vars: list of sampling variables
shared: dict of theano variable -> shared variable
blocked: Boolean (default True)
class pymc3.step_methods.metropolis.DEMetropolisZ(*args, **kwargs)

Adaptive Differential Evolution Metropolis sampling step that uses the past to inform jumps.

Parameters
lamb: float

Lambda parameter of the DE proposal mechanism. Defaults to 2.38 / sqrt(2 * ndim)

vars: list

List of variables for sampler

S: standard deviation or covariance matrix

Some measure of variance to parameterize proposal distribution

proposal_dist: function

Function that returns zero-mean deviates when parameterized with S (and n). Defaults to Uniform(-S,+S).

scaling: scalar or array

Initial scale factor for epsilon. Defaults to 0.001

tune: str

Which hyperparameter to tune. Defaults to ‘lambda’, but can also be ‘scaling’ or None.

tune_interval: int

The frequency of tuning. Defaults to 100 iterations.

tune_drop_fraction: float

Fraction of tuning steps that will be removed from the samplers history when the tuning ends. Defaults to 0.9 - keeping the last 10% of tuning steps for good mixing while removing 90% of potentially unconverged tuning positions.

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

mode: string or `Mode` instance.

compilation mode passed to Theano functions

References

Braak2006

Cajo C.F. ter Braak (2006). Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing link

Parameters
vars: list of sampling variables
shared: dict of theano variable -> shared variable
blocked: Boolean (default True)
reset_tuning()

Resets the tuned sampler parameters and history to their initial values.

stop_tuning()

At the end of the tuning phase, this method removes the first x% of the history so future proposals are not informed by unconverged tuning iterations.

class pymc3.step_methods.metropolis.Metropolis(*args, **kwargs)

Metropolis-Hastings sampling step

Create an instance of a Metropolis stepper

Parameters
vars: list

List of variables for sampler

S: standard deviation or covariance matrix

Some measure of variance to parameterize proposal distribution

proposal_dist: function

Function that returns zero-mean deviates when parameterized with S (and n). Defaults to normal.

scaling: scalar or array

Initial scale factor for proposal. Defaults to 1.

tune: bool

Flag for tuning. Defaults to True.

tune_interval: int

The frequency of tuning. Defaults to 100 iterations.

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

mode: string or `Mode` instance.

compilation mode passed to Theano functions

reset_tuning()

Resets the tuned sampler parameters to their initial values.

Slice

class pymc3.step_methods.slicer.Slice(*args, **kwargs)

Univariate slice sampler step method

Parameters
vars: list

List of variables for sampler.

w: float

Initial width of slice (Defaults to 1).

tune: bool

Flag for tuning (Defaults to True).

model: PyMC Model

Optional model for sampling step. Defaults to None (taken from context).

Hamiltonian Monte Carlo

class pymc3.step_methods.hmc.hmc.HamiltonianMC(*args, **kwargs)

A sampler for continuous variables based on Hamiltonian mechanics.

See NUTS sampler for automatically tuned stopping time and step size scaling.

Set up the Hamiltonian Monte Carlo sampler.

Parameters
vars: list of theano variables
path_length: float, default=2

total length to travel

step_rand: function float -> float, default=unif

A function which takes the step size and returns an new one used to randomize the step size at each iteration.

step_scale: float, default=0.25

Initial size of steps to take, automatically scaled down by 1/n**(1/4).

scaling: array_like, ndim = {1,2}

The inverse mass, or precision matrix. One dimensional arrays are interpreted as diagonal matrices. If is_cov is set to True, this will be interpreded as the mass or covariance matrix.

is_cov: bool, default=False

Treat the scaling as mass or covariance matrix.

potential: Potential, optional

An object that represents the Hamiltonian with methods velocity, energy, and random methods. It can be specified instead of the scaling matrix.

target_accept: float, default 0.65

Adapt the step size such that the average acceptance probability across the trajectories are close to target_accept. Higher values for target_accept lead to smaller step sizes. Setting this to higher values like 0.9 or 0.99 can help with sampling from difficult posteriors. Valid values are between 0 and 1 (exclusive). Default of 0.65 is from (Beskos et. al. 2010, Neal 2011). See Hoffman and Gelman’s “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo” section 3.2 for details.

gamma: float, default .05
k: float, default .75

Parameter for dual averaging for step size adaptation. Values between 0.5 and 1 (exclusive) are admissible. Higher values correspond to slower adaptation.

t0: float > 0, default 10

Parameter for dual averaging. Higher values slow initial adaptation.

adapt_step_size: bool, default=True

Whether step size adaptation should be enabled. If this is disabled, k, t0, gamma and target_accept are ignored.

max_steps: int

The maximum number of leapfrog steps.

model: pymc3.Model

The model

**kwargs: passed to BaseHMC
static competence(var, has_grad)

Check how appropriate this class is for sampling a random variable.

Sequential Monte Carlo

class pymc3.smc.smc.SMC(draws=2000, kernel='metropolis', n_steps=25, start=None, tune_steps=True, p_acc_rate=0.85, threshold=0.5, save_sim_data=False, save_log_pseudolikelihood=True, model=None, random_seed=- 1, chain=0)

Sequential Monte Carlo with Independent Metropolis-Hastings and ABC kernels.

initialize_logp()

Initialize the prior and likelihood log probabilities.

initialize_population()

Create an initial population from the prior distribution.

mutate()

Independent Metropolis-Hastings perturbation.

posterior_to_trace()

Save results into a PyMC3 trace.

resample()

Resample particles based on importance weights.

setup_kernel()

Set up the likelihood logp function based on the chosen kernel.

tune()

Tune n_steps based on the acceptance rate.

update_proposal()

Update proposal based on the covariance matrix from tempered posterior.

update_weights_beta()

Calculate the next inverse temperature (beta).

The importance weights based on current beta and tempered likelihood and updates the marginal likelihood estimate.

MultiTrace

class pymc3.backends.base.MultiTrace(straces)

Main interface for accessing values from MCMC results.

The core method to select values is get_values. The method to select sampler statistics is get_sampler_stats. Both kinds of values can also be accessed by indexing the MultiTrace object. Indexing can behave in four ways:

  1. Indexing with a variable or variable name (str) returns all values for that variable, combining values for all chains.

    >>> trace[varname]
    

    Slicing after the variable name can be used to burn and thin the samples.

    >>> trace[varname, 1000:]
    

    For convenience during interactive use, values can also be accessed using the variable as an attribute.

    >>> trace.varname
    
  2. Indexing with an integer returns a dictionary with values for each variable at the given index (corresponding to a single sampling iteration).

  3. Slicing with a range returns a new trace with the number of draws corresponding to the range.

  4. Indexing with the name of a sampler statistic that is not also the name of a variable returns those values from all chains. If there is more than one sampler that provides that statistic, the values are concatenated along a new axis.

For any methods that require a single trace (e.g., taking the length of the MultiTrace instance, which returns the number of draws), the trace with the highest chain number is always used.

Attributes
nchains: int

Number of chains in the MultiTrace.

chains: `List[int]`

List of chain indices

report: str

Report on the sampling process.

varnames: `List[str]`

List of variable names in the trace(s)

add_values(vals, overwrite=False) None

Add variables to traces.

Parameters
vals: dict (str: array-like)

The keys should be the names of the new variables. The values are expected to be array-like objects. For traces with more than one chain the length of each value should match the number of total samples already in the trace (chains * iterations), otherwise a warning is raised.

overwrite: bool

If False (default) a ValueError is raised if the variable already exists. Change to True to overwrite the values of variables

Returns
None.
get_sampler_stats(stat_name, burn=0, thin=1, combine=True, chains=None, squeeze=True)

Get sampler statistics from the trace.

Parameters
stat_name: str
sampler_idx: int or None
burn: int
thin: int
Returns
If the sampler_idx is specified, return the statistic with
the given name in a numpy array. If it is not specified and there
is more than one sampler that provides this statistic, return
a numpy array of shape (m, n), where m is the number of
such samplers, and n is the number of samples.
get_values(varname, burn=0, thin=1, combine=True, chains=None, squeeze=True)

Get values from traces.

Parameters
varname: str
burn: int
thin: int
combine: bool

If True, results from chains will be concatenated.

chains: int or list of ints

Chains to retrieve. If None, all chains are used. A single chain value can also be given.

squeeze: bool

Return a single array element if the resulting list of values only has one element. If False, the result will always be a list of arrays, even if combine is True.

Returns
A list of NumPy arrays or a single NumPy array (depending on
squeeze).
point(idx, chain=None)

Return a dictionary of point values at idx.

Parameters
idx: int
chain: int

If a chain is not given, the highest chain number is used.

points(chains=None)

Return an iterator over all or some of the sample points

Parameters
chains: list of int or N

The chains whose points should be inlcuded in the iterator. If chains is not given, include points from all chains.

remove_values(name)

remove variables from traces.

Parameters
name: str

Name of the variable to remove. Raises KeyError if the variable is not present

class pymc3.backends.base.BaseTrace(name, model=None, vars=None, test_point=None)

Base trace object

Parameters
name: str

Name of backend

model: Model

If None, the model is taken from the with context.

vars: list of variables

Sampling values will be stored for these variables. If None, model.unobserved_RVs is used.

test_point: dict

use different test point that might be with changed variables shapes

Variational Inference

OPVI

Variational inference is a great approach for doing really complex, often intractable Bayesian inference in approximate form. Common methods (e.g. ADVI) lack from complexity so that approximate posterior does not reveal the true nature of underlying problem. In some applications it can yield unreliable decisions.

Recently on NIPS 2017 OPVI framework was presented. It generalizes variational inference so that the problem is build with blocks. The first and essential block is Model itself. Second is Approximation, in some cases \(log Q(D)\) is not really needed. Necessity depends on the third and fourth part of that black box, Operator and Test Function respectively.

Operator is like an approach we use, it constructs loss from given Model, Approximation and Test Function. The last one is not needed if we minimize KL Divergence from Q to posterior. As a drawback we need to compute \(loq Q(D)\). Sometimes approximation family is intractable and \(loq Q(D)\) is not available, here comes LS(Langevin Stein) Operator with a set of test functions.

Test Function has more unintuitive meaning. It is usually used with LS operator and represents all we want from our approximate distribution. For any given vector based function of \(z\) LS operator yields zero mean function under posterior. \(loq Q(D)\) is no more needed. That opens a door to rich approximation families as neural networks.

References

class pymc3.variational.opvi.Approximation(groups, model=None)

Wrapper for grouped approximations

Wraps list of groups, creates an Approximation instance that collects sampled variables from all the groups, also collects logQ needed for explicit Variational Inference.

Parameters
groups: list[Group]

List of Group instances. They should have all model variables

model: Model

See also

Group

Notes

Some shortcuts for single group approximations are available:

  • MeanField

  • FullRank

  • NormalizingFlow

  • Empirical

Single group accepts local_rv keyword with dict mapping PyMC3 variables to their local Group parameters dict

property datalogp

Dev - computes \(E_{q}(data term)\) from model via theano.scan that can be optimized later

property datalogp_norm

Dev - normalized \(E_{q}(data term)\)

get_optimization_replacements(s, d)

Dev - optimizations for logP. If sample size is static and equal to 1: then theano.scan MC estimate is replaced with single sample without call to theano.scan.

property logp

Dev - computes \(E_{q}(logP)\) from model via theano.scan that can be optimized later

property logp_norm

Dev - normalized \(E_{q}(logP)\)

property logq

Dev - collects logQ for all groups

property logq_norm

Dev - collects logQ for all groups and normalizes it

make_size_and_deterministic_replacements(s, d, more_replacements=None)

Dev - creates correct replacements for initial depending on sample size and deterministic flag

Parameters
s: scalar

sample size

d: bool

whether sampling is done deterministically

more_replacements: dict

replacements for shape and initial

Returns
dict with replacements for initial
property replacements

Dev - all replacements from groups to replace PyMC random variables with approximation

rslice(name)

Dev - vectorized sampling for named random variable without call to theano.scan. This node still needs set_size_and_deterministic() to be evaluated

sample(draws=500, include_transformed=True)

Draw samples from variational posterior.

Parameters
draws: `int`

Number of random samples.

include_transformed: `bool`

If True, transformed variables are also sampled. Default is False.

Returns
trace: pymc3.backends.base.MultiTrace

Samples drawn from variational posterior.

sample_node(node, size=None, deterministic=False, more_replacements=None)

Samples given node or nodes over shared posterior

Parameters
node: Theano Variables (or Theano expressions)
size: None or scalar

number of samples

more_replacements: `dict`

add custom replacements to graph, e.g. change input source

deterministic: bool

whether to use zeros as initial distribution if True - zero initial point will produce constant latent variables

Returns
sampled node(s) with replacements
property scale_cost_to_minibatch

Dev - Property to control scaling cost to minibatch

set_size_and_deterministic(node, s, d, more_replacements=None)

Dev - after node is sampled via symbolic_sample_over_posterior() or symbolic_single_sample() new random generator can be allocated and applied to node

Parameters
node: :class:`Variable`

Theano node with symbolically applied VI replacements

s: scalar

desired number of samples

d: bool or int

whether sampling is done deterministically

more_replacements: dict

more replacements to apply

Returns
Variable with applied replacements, ready to use
property single_symbolic_datalogp

Dev - for single MC sample estimate of \(E_{q}(data term)\) theano.scan is not needed and code can be optimized

property single_symbolic_logp

Dev - for single MC sample estimate of \(E_{q}(logP)\) theano.scan is not needed and code can be optimized

property single_symbolic_varlogp

Dev - for single MC sample estimate of \(E_{q}(prior term)\) theano.scan is not needed and code can be optimized

property sized_symbolic_datalogp

Dev - computes sampled data term from model via theano.scan

property sized_symbolic_logp

Dev - computes sampled logP from model via theano.scan

property sized_symbolic_varlogp

Dev - computes sampled prior term from model via theano.scan

property symbolic_logq

Dev - collects symbolic_logq for all groups

property symbolic_normalizing_constant

Dev - normalizing constant for self.logq, scales it to minibatch_size instead of total_size. Here the effect is controlled by self.scale_cost_to_minibatch

symbolic_sample_over_posterior(node)

Dev - performs sampling of node applying independent samples from posterior each time. Note that it is done symbolically and this node needs set_size_and_deterministic() call

symbolic_single_sample(node)

Dev - performs sampling of node applying single sample from posterior. Note that it is done symbolically and this node needs set_size_and_deterministic() call with size=1

to_flat_input(node)

Dev - replace vars with flattened view stored in self.inputs

property varlogp

Dev - computes \(E_{q}(prior term)\) from model via theano.scan that can be optimized later

property varlogp_norm

Dev - normalized \(E_{q}(prior term)\)

class pymc3.variational.opvi.Group(group=None, vfam=None, params=None, *args, **kwargs)

Base class for grouping variables in VI

Grouped Approximation is used for modelling mutual dependencies for a specified group of variables. Base for local and global group.

Parameters
group: list

List of PyMC3 variables or None indicating that group takes all the rest variables

vfam: str

String that marks the corresponding variational family for the group. Cannot be passed both with params

params: dict

Dict with variational family parameters, full description can be found below. Cannot be passed both with vfam

random_seed: int

Random seed for underlying random generator

model :

PyMC3 Model

local: bool

Indicates whether this group is local. Cannot be passed without params. Such group should have only one variable

rowwise: bool

Indicates whether this group is independently parametrized over first dim. Such group should have only one variable

options: dict

Special options for the group

kwargs: Other kwargs for the group

See also

Approximation

Notes

Group instance/class has some important constants:

  • supports_batched Determines whether such variational family can be used for AEVB or rowwise approx.

    AEVB approx is such approx that somehow depends on input data. It can be treated as conditional distribution. You can see more about in the corresponding paper mentioned in references.

    Rowwise mode is a special case approximation that treats every ‘row’, of a tensor as independent from each other. Some distributions can’t do that by definition e.g. Empirical that consists of particles only.

  • has_logq Tells that distribution is defined explicitly

These constants help providing the correct inference method for given parametrization

References

Examples

Basic Initialization

Group is a factory class. You do not need to call every ApproximationGroup explicitly. Passing the correct vfam (Variational FAMily) argument you’ll tell what parametrization is desired for the group. This helps not to overload code with lots of classes.

>>> group = Group([latent1, latent2], vfam='mean_field')

The other way to select approximation is to provide params dictionary that has some predefined well shaped parameters. Keys of the dict serve as an identifier for variational family and help to autoselect the correct group class. To identify what approximation to use, params dict should have the full set of needed parameters. As there are 2 ways to instantiate the Group passing both vfam and params is prohibited. Partial parametrization is prohibited by design to avoid corner cases and possible problems.

>>> group = Group([latent3], params=dict(mu=my_mu, rho=my_rho))

Important to note that in case you pass custom params they will not be autocollected by optimizer, you’ll have to provide them with more_obj_params keyword.

Supported dict keys:

  • {‘mu’, ‘rho’}: MeanFieldGroup

  • {‘mu’, ‘L_tril’}: FullRankGroup

  • {‘histogram’}: EmpiricalGroup

  • {0, 1, 2, 3, …, k-1}: NormalizingFlowGroup of depth k

    NormalizingFlows have other parameters than ordinary groups and should be passed as nested dicts with the following keys:

    • {‘u’, ‘w’, ‘b’}: PlanarFlow

    • {‘a’, ‘b’, ‘z_ref’}: RadialFlow

    • {‘loc’}: LocFlow

    • {‘rho’}: ScaleFlow

    • {‘v’}: HouseholderFlow

    Note that all integer keys should be present in the dictionary. An example of NormalizingFlow initialization can be found below.

Using AEVB

Autoencoding variational Bayes is a powerful tool to get conditional \(q(\lambda|X)\) distribution on latent variables. It is well supported by PyMC3 and all you need is to provide a dictionary with well shaped variational parameters, the correct approximation will be autoselected as mentioned in section above. However we have some implementation restrictions in AEVB. They require autoencoded variable to have first dimension as batch dimension and other dimensions should stay fixed. With this assumptions it is possible to generalize all variational approximation families as batched approximations that have flexible parameters and leading axis.

Only single variable local group is supported. Params are required.

>>> # for mean field
>>> group = Group([latent3], params=dict(mu=my_mu, rho=my_rho), local=True)
>>> # or for full rank
>>> group = Group([latent3], params=dict(mu=my_mu, L_tril=my_L_tril), local=True)
  • An Approximation class is selected automatically based on the keys in dict.

  • my_mu and my_rho are usually estimated with neural network or function approximator.

Using Row-Wise Group

Batch groups have independent row wise approximations, thus using batched mean field will give no effect. It is more interesting if you want each row of a matrix to be parametrized independently with normalizing flow or full rank gaussian.

To tell Group that group is batched you need set batched kwarg as True. Only single variable group is allowed due to implementation details.

>>> group = Group([latent3], vfam='fr', rowwise=True) # 'fr' is alias for 'full_rank'

The resulting approximation for this variable will have the following structure

\[latent3_{i, \dots} \sim \mathcal{N}(\mu_i, \Sigma_i) \forall i\]

Note: Using rowwise and user-parametrized approximation is ok, but shape should be checked beforehand, it is impossible to infer it by PyMC3

Normalizing Flow Group

In case you use simple initialization pattern using vfam you’ll not meet any changes. Passing flow formula to vfam you’ll get correct flow parametrization for group

>>> group = Group([latent3], vfam='scale-hh*5-radial*4-loc')

Note: Consider passing location flow as the last one and scale as the first one for stable inference.

Rowwise normalizing flow is supported as well

>>> group = Group([latent3], vfam='scale-hh*2-radial-loc', rowwise=True)

Custom parameters for normalizing flow can be a real trouble for the first time. They have quite different format from the rest variational families.

>>> # int is used as key, it also tells the flow position
... flow_params = {
...     # `rho` parametrizes scale flow, softplus is used to map (-inf; inf) -> (0, inf)
...     0: dict(rho=my_scale),
...     1: dict(v=my_v1),  # Householder Flow, `v` is parameter name from the original paper
...     2: dict(v=my_v2),  # do not miss any number in dict, or else error is raised
...     3: dict(a=my_a, b=my_b, z_ref=my_z_ref),  # Radial flow
...     4: dict(loc=my_loc)  # Location Flow
... }
... group = Group([latent3], params=flow_params)
... # local=True can be added in case you do AEVB inference
... group = Group([latent3], params=flow_params, local=True)

Delayed Initialization

When you have a lot of latent variables it is impractical to do it all manually. To make life much simpler, You can pass None instead of list of variables. That case you’ll not create shared parameters until you pass all collected groups to Approximation object that collects all the groups together and checks that every group is correctly initialized. For those groups which have group equal to None it will collect all the rest variables not covered by other groups and perform delayed init.

>>> group_1 = Group([latent1], vfam='fr')  # latent1 has full rank approximation
>>> group_other = Group(None, vfam='mf')  # other variables have mean field Q
>>> approx = Approximation([group_1, group_other])

Summing Up

When you have created all the groups they need to pass all the groups to Approximation. It does not accept any other parameter rather than groups

>>> approx = Approximation(my_groups)
property logq

Dev - Monte Carlo estimate for group logQ

property logq_norm

Dev - Monte Carlo estimate for group logQ normalized

make_size_and_deterministic_replacements(s, d, more_replacements=None)

Dev - creates correct replacements for initial depending on sample size and deterministic flag

Parameters
s: scalar

sample size

d: bool or scalar

whether sampling is done deterministically

more_replacements: dict

replacements for shape and initial

Returns
dict with replacements for initial
set_size_and_deterministic(node, s, d, more_replacements=None)

Dev - after node is sampled via symbolic_sample_over_posterior() or symbolic_single_sample() new random generator can be allocated and applied to node

Parameters
node: :class:`Variable`

Theano node with symbolically applied VI replacements

s: scalar

desired number of samples

d: bool or int

whether sampling is done deterministically

more_replacements: dict

more replacements to apply

Returns
Variable with applied replacements, ready to use
property symbolic_logq

Dev - correctly scaled self.symbolic_logq_not_scaled

property symbolic_logq_not_scaled

Dev - symbolically computed logq for self.symbolic_random computations can be more efficient since all is known beforehand including self.symbolic_random

property symbolic_normalizing_constant

Dev - normalizing constant for self.logq, scales it to minibatch_size instead of total_size

property symbolic_random

Dev - abstract node that takes self.symbolic_initial and creates approximate posterior that is parametrized with self.params_dict.

Implementation should take in account self.batched. If self.batched is True, then self.symbolic_initial is 3d tensor, else 2d

Returns
tensor
property symbolic_random2d

Dev - self.symbolic_random flattened to matrix

symbolic_sample_over_posterior(node)

Dev - performs sampling of node applying independent samples from posterior each time. Note that it is done symbolically and this node needs set_size_and_deterministic() call

symbolic_single_sample(node)

Dev - performs sampling of node applying single sample from posterior. Note that it is done symbolically and this node needs set_size_and_deterministic() call with size=1

to_flat_input(node)

Dev - replace vars with flattened view stored in self.inputs

class pymc3.variational.opvi.ObjectiveFunction(op, tf)

Helper class for construction loss and updates for variational inference

Parameters
opOperator

OPVI Functional operator

tfTestFunction

OPVI TestFunction

score_function(sc_n_mc=None, more_replacements=None, fn_kwargs=None)

Compile scoring function that operates which takes no inputs and returns Loss

Parameters
sc_n_mc: `int`

number of scoring MC samples

more_replacements:

Apply custom replacements before compiling a function

fn_kwargs: `dict`

arbitrary kwargs passed to theano.function

Returns
theano.function
step_function(obj_n_mc=None, tf_n_mc=None, obj_optimizer=<function adagrad_window>, test_optimizer=<function adagrad_window>, more_obj_params=None, more_tf_params=None, more_updates=None, more_replacements=None, total_grad_norm_constraint=None, score=False, fn_kwargs=None)

Step function that should be called on each optimization step.

Generally it solves the following problem:

\[\mathbf{\lambda^{\*}} = \inf_{\lambda} \sup_{\theta} t(\mathbb{E}_{\lambda}[(O^{p,q}f_{\theta})(z)])\]
Parameters
obj_n_mc: `int`

Number of monte carlo samples used for approximation of objective gradients

tf_n_mc: `int`

Number of monte carlo samples used for approximation of test function gradients

obj_optimizer: function (grads, params) -> updates

Optimizer that is used for objective params

test_optimizer: function (grads, params) -> updates

Optimizer that is used for test function params

more_obj_params: `list`

Add custom params for objective optimizer

more_tf_params: `list`

Add custom params for test function optimizer

more_updates: `dict`

Add custom updates to resulting updates

total_grad_norm_constraint: `float`

Bounds gradient norm, prevents exploding gradient problem

score: `bool`

calculate loss on each step? Defaults to False for speed

fn_kwargs: `dict`

Add kwargs to theano.function (e.g. {‘profile’: True})

more_replacements: `dict`

Apply custom replacements before calculating gradients

Returns
theano.function
updates(obj_n_mc=None, tf_n_mc=None, obj_optimizer=<function adagrad_window>, test_optimizer=<function adagrad_window>, more_obj_params=None, more_tf_params=None, more_updates=None, more_replacements=None, total_grad_norm_constraint=None)

Calculate gradients for objective function, test function and then constructs updates for optimization step

Parameters
obj_n_mcint

Number of monte carlo samples used for approximation of objective gradients

tf_n_mcint

Number of monte carlo samples used for approximation of test function gradients

obj_optimizerfunction (loss, params) -> updates

Optimizer that is used for objective params

test_optimizerfunction (loss, params) -> updates

Optimizer that is used for test function params

more_obj_paramslist

Add custom params for objective optimizer

more_tf_paramslist

Add custom params for test function optimizer

more_updatesdict

Add custom updates to resulting updates

more_replacementsdict

Apply custom replacements before calculating gradients

total_grad_norm_constraintfloat

Bounds gradient norm, prevents exploding gradient problem

Returns
ObjectiveUpdates
class pymc3.variational.opvi.Operator(approx)

Base class for Operator

Parameters
approx: :class:`Approximation`

an approximation instance

Notes

For implementing custom operator it is needed to define Operator.apply() method

apply(f)

Operator itself

\[(O^{p,q}f_{\theta})(z)\]
Parameters
f: :class:`TestFunction` or None

function that takes z = self.input and returns same dimensional output

Returns
TensorVariable

symbolically applied operator

objective_class

alias of pymc3.variational.opvi.ObjectiveFunction

VI Inference API

class pymc3.variational.inference.ADVI(*args, **kwargs)

Automatic Differentiation Variational Inference (ADVI)

This class implements the meanfield ADVI, where the variational posterior distribution is assumed to be spherical Gaussian without correlation of parameters and fit to the true posterior distribution. The means and standard deviations of the variational posterior are referred to as variational parameters.

For explanation, we classify random variables in probabilistic models into three types. Observed random variables \({\cal Y}=\{\mathbf{y}_{i}\}_{i=1}^{N}\) are \(N\) observations. Each \(\mathbf{y}_{i}\) can be a set of observed random variables, i.e., \(\mathbf{y}_{i}=\{\mathbf{y}_{i}^{k}\}_{k=1}^{V_{o}}\), where \(V_{k}\) is the number of the types of observed random variables in the model.

The next ones are global random variables \(\Theta=\{\theta^{k}\}_{k=1}^{V_{g}}\), which are used to calculate the probabilities for all observed samples.

The last ones are local random variables \({\cal Z}=\{\mathbf{z}_{i}\}_{i=1}^{N}\), where \(\mathbf{z}_{i}=\{\mathbf{z}_{i}^{k}\}_{k=1}^{V_{l}}\). These RVs are used only in AEVB.

The goal of ADVI is to approximate the posterior distribution \(p(\Theta,{\cal Z}|{\cal Y})\) by variational posterior \(q(\Theta)\prod_{i=1}^{N}q(\mathbf{z}_{i})\). All of these terms are normal distributions (mean-field approximation).

\(q(\Theta)\) is parametrized with its means and standard deviations. These parameters are denoted as \(\gamma\). While \(\gamma\) is a constant, the parameters of \(q(\mathbf{z}_{i})\) are dependent on each observation. Therefore these parameters are denoted as \(\xi(\mathbf{y}_{i}; \nu)\), where \(\nu\) is the parameters of \(\xi(\cdot)\). For example, \(\xi(\cdot)\) can be a multilayer perceptron or convolutional neural network.

In addition to \(\xi(\cdot)\), we can also include deterministic mappings for the likelihood of observations. We denote the parameters of the deterministic mappings as \(\eta\). An example of such mappings is the deconvolutional neural network used in the convolutional VAE example in the PyMC3 notebook directory.

This function maximizes the evidence lower bound (ELBO) \({\cal L}(\gamma, \nu, \eta)\) defined as follows:

\[\begin{split}{\cal L}(\gamma,\nu,\eta) & = \mathbf{c}_{o}\mathbb{E}_{q(\Theta)}\left[ \sum_{i=1}^{N}\mathbb{E}_{q(\mathbf{z}_{i})}\left[ \log p(\mathbf{y}_{i}|\mathbf{z}_{i},\Theta,\eta) \right]\right] \\ & - \mathbf{c}_{g}KL\left[q(\Theta)||p(\Theta)\right] - \mathbf{c}_{l}\sum_{i=1}^{N} KL\left[q(\mathbf{z}_{i})||p(\mathbf{z}_{i})\right],\end{split}\]

where \(KL[q(v)||p(v)]\) is the Kullback-Leibler divergence

\[KL[q(v)||p(v)] = \int q(v)\log\frac{q(v)}{p(v)}dv,\]

\(\mathbf{c}_{o/g/l}\) are vectors for weighting each term of ELBO. More precisely, we can write each of the terms in ELBO as follows:

\[\begin{split}\mathbf{c}_{o}\log p(\mathbf{y}_{i}|\mathbf{z}_{i},\Theta,\eta) & = & \sum_{k=1}^{V_{o}}c_{o}^{k} \log p(\mathbf{y}_{i}^{k}| {\rm pa}(\mathbf{y}_{i}^{k},\Theta,\eta)) \\ \mathbf{c}_{g}KL\left[q(\Theta)||p(\Theta)\right] & = & \sum_{k=1}^{V_{g}}c_{g}^{k}KL\left[ q(\theta^{k})||p(\theta^{k}|{\rm pa(\theta^{k})})\right] \\ \mathbf{c}_{l}KL\left[q(\mathbf{z}_{i}||p(\mathbf{z}_{i})\right] & = & \sum_{k=1}^{V_{l}}c_{l}^{k}KL\left[ q(\mathbf{z}_{i}^{k})|| p(\mathbf{z}_{i}^{k}|{\rm pa}(\mathbf{z}_{i}^{k}))\right],\end{split}\]

where \({\rm pa}(v)\) denotes the set of parent variables of \(v\) in the directed acyclic graph of the model.

When using mini-batches, \(c_{o}^{k}\) and \(c_{l}^{k}\) should be set to \(N/M\), where \(M\) is the number of observations in each mini-batch. This is done with supplying total_size parameter to observed nodes (e.g. Normal('x', 0, 1, observed=data, total_size=10000)). In this case it is possible to automatically determine appropriate scaling for \(logp\) of observed nodes. Interesting to note that it is possible to have two independent observed variables with different total_size and iterate them independently during inference.

For working with ADVI, we need to give

  • The probabilistic model

    model with three types of RVs (observed_RVs, global_RVs and local_RVs).

  • (optional) Minibatches

    The tensors to which mini-bathced samples are supplied are handled separately by using callbacks in Inference.fit() method that change storage of shared theano variable or by pymc3.generator() that automatically iterates over minibatches and defined beforehand.

  • (optional) Parameters of deterministic mappings

    They have to be passed along with other params to Inference.fit() method as more_obj_params argument.

For more information concerning training stage please reference pymc3.variational.opvi.ObjectiveFunction.step_function()

Parameters
local_rv: dict[var->tuple]

mapping {model_variable -> approx params} Local Vars are used for Autoencoding Variational Bayes See (AEVB; Kingma and Welling, 2014) for details

model: :class:`pymc3.Model`

PyMC3 model for inference

random_seed: None or int

leave None to use package global RandomStream or other valid value to create instance specific one

start: `Point`

starting point for inference

References

  • Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2016). Automatic Differentiation Variational Inference. arXiv preprint arXiv:1603.00788.

  • Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI approximateinference.org/accepted/RoederEtAl2016.pdf

  • Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. stat, 1050, 1.

class pymc3.variational.inference.ASVGD(approx=None, estimator=<class 'pymc3.variational.operators.KSD'>, kernel=<pymc3.variational.test_functions.RBF object>, **kwargs)

Amortized Stein Variational Gradient Descent

not suggested to use

This inference is based on Kernelized Stein Discrepancy it’s main idea is to move initial noisy particles so that they fit target distribution best.

Algorithm is outlined below

Input: Parametrized random generator \(R_{\theta}\)

Output: \(R_{\theta^{*}}\) that approximates the target distribution.

\[\begin{split}\Delta x_i &= \hat{\phi}^{*}(x_i) \\ \hat{\phi}^{*}(x) &= \frac{1}{n}\sum^{n}_{j=1}[k(x_j,x) \nabla_{x_j} logp(x_j)+ \nabla_{x_j} k(x_j,x)] \\ \Delta_{\theta} &= \frac{1}{n}\sum^{n}_{i=1}\Delta x_i\frac{\partial x_i}{\partial \theta}\end{split}\]
Parameters
approx: :class:`Approximation`

default is FullRank but can be any

kernel: `callable`

kernel function for KSD \(f(histogram) -> (k(x,.), \nabla_x k(x,.))\)

model: :class:`Model`
kwargs: kwargs for gradient estimator

References

  • Dilin Wang, Yihao Feng, Qiang Liu (2016) Learning to Sample Using Stein Discrepancy http://bayesiandeeplearning.org/papers/BDL_21.pdf

  • Dilin Wang, Qiang Liu (2016) Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning arXiv:1611.01722

  • Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng (2017) Stein Variational Policy Gradient arXiv:1704.02399

fit(n=10000, score=None, callbacks=None, progressbar=True, obj_n_mc=500, **kwargs)

Perform Operator Variational Inference

Parameters
n: int

number of iterations

score: bool

evaluate loss on each iteration or not

callbacks: list[function: (Approximation, losses, i) -> None]

calls provided functions after each iteration step

progressbar: bool

whether to show progressbar or not

Returns
Approximation
Other Parameters
obj_n_mc: `int`

Number of monte carlo samples used for approximation of objective gradients

tf_n_mc: `int`

Number of monte carlo samples used for approximation of test function gradients

obj_optimizer: function (grads, params) -> updates

Optimizer that is used for objective params

test_optimizer: function (grads, params) -> updates

Optimizer that is used for test function params

more_obj_params: `list`

Add custom params for objective optimizer

more_tf_params: `list`

Add custom params for test function optimizer

more_updates: `dict`

Add custom updates to resulting updates

total_grad_norm_constraint: `float`

Bounds gradient norm, prevents exploding gradient problem

fn_kwargs: `dict`

Add kwargs to theano.function (e.g. {‘profile’: True})

more_replacements: `dict`

Apply custom replacements before calculating gradients

class pymc3.variational.inference.FullRankADVI(*args, **kwargs)

Full Rank Automatic Differentiation Variational Inference (ADVI)

Parameters
local_rv: dict[var->tuple]

mapping {model_variable -> approx params} Local Vars are used for Autoencoding Variational Bayes See (AEVB; Kingma and Welling, 2014) for details

model: :class:`pymc3.Model`

PyMC3 model for inference

random_seed: None or int

leave None to use package global RandomStream or other valid value to create instance specific one

start: `Point`

starting point for inference

References

  • Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., and Blei, D. M. (2016). Automatic Differentiation Variational Inference. arXiv preprint arXiv:1603.00788.

  • Geoffrey Roeder, Yuhuai Wu, David Duvenaud, 2016 Sticking the Landing: A Simple Reduced-Variance Gradient for ADVI approximateinference.org/accepted/RoederEtAl2016.pdf

  • Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. stat, 1050, 1.

class pymc3.variational.inference.ImplicitGradient(approx, estimator=<class 'pymc3.variational.operators.KSD'>, kernel=<pymc3.variational.test_functions.RBF object>, **kwargs)

Implicit Gradient for Variational Inference

not suggested to use

An approach to fit arbitrary approximation by computing kernel based gradient By default RBF kernel is used for gradient estimation. Default estimator is Kernelized Stein Discrepancy with temperature equal to 1. This temperature works only for large number of samples. Larger temperature is needed for small number of samples but there is no theoretical approach to choose the best one in such case.

class pymc3.variational.inference.Inference(op, approx, tf, **kwargs)

Base class for Variational Inference

Communicates Operator, Approximation and Test Function to build Objective Function

Parameters
op: Operator class
approx: Approximation class or instance
tf: TestFunction instance
model: Model

PyMC3 Model

kwargs: kwargs passed to :class:`Operator`
fit(n=10000, score=None, callbacks=None, progressbar=True, **kwargs)

Perform Operator Variational Inference

Parameters
n: int

number of iterations

score: bool

evaluate loss on each iteration or not

callbacks: list[function: (Approximation, losses, i) -> None]

calls provided functions after each iteration step

progressbar: bool

whether to show progressbar or not

Returns
Approximation
Other Parameters
obj_n_mc: `int`

Number of monte carlo samples used for approximation of objective gradients

tf_n_mc: `int`

Number of monte carlo samples used for approximation of test function gradients

obj_optimizer: function (grads, params) -> updates

Optimizer that is used for objective params

test_optimizer: function (grads, params) -> updates

Optimizer that is used for test function params

more_obj_params: `list`

Add custom params for objective optimizer

more_tf_params: `list`

Add custom params for test function optimizer

more_updates: `dict`

Add custom updates to resulting updates

total_grad_norm_constraint: `float`

Bounds gradient norm, prevents exploding gradient problem

fn_kwargs: `dict`

Add kwargs to theano.function (e.g. {‘profile’: True})

more_replacements: `dict`

Apply custom replacements before calculating gradients

refine(n, progressbar=True)

Refine the solution using the last compiled step function

class pymc3.variational.inference.KLqp(approx, beta=1.0)

Kullback Leibler Divergence Inference

General approach to fit Approximations that define \(logq\) by maximizing ELBO (Evidence Lower Bound). In some cases rescaling the regularization term KL may be beneficial

\[ELBO_\beta = \log p(D|\theta) - \beta KL(q||p)\]
Parameters
approx: :class:`Approximation`

Approximation to fit, it is required to have logQ

beta: float

Scales the regularization term in ELBO (see Christopher P. Burgess et al., 2017)

References

  • Christopher P. Burgess et al. (NIPS, 2017) Understanding disentangling in \(\beta\)-VAE arXiv preprint 1804.03599

class pymc3.variational.inference.NFVI(*args, **kwargs)

Normalizing Flow based :class:`KLqp` inference

Normalizing flow is a series of invertible transformations on initial distribution.

\[z_K = f_K \circ \dots \circ f_2 \circ f_1(z_0)\]

In that case we can compute tractable density for the flow.

\[\ln q_K(z_K) = \ln q_0(z_0) - \sum_{k=1}^{K}\ln \left|\frac{\partial f_k}{\partial z_{k-1}}\right|\]

Every \(f_k\) here is a parametric function with defined determinant. We can choose every step here. For example the here is a simple flow is an affine transform:

\[z = loc(scale(z_0)) = \mu + \sigma * z_0\]

Here we get mean field approximation if \(z_0 \sim \mathcal{N}(0, 1)\)

Flow Formulas

In PyMC3 there is a flexible way to define flows with formulas. We have 5 of them by the moment:

  • Loc (loc): \(z' = z + \mu\)

  • Scale (scale): \(z' = \sigma * z\)

  • Planar (planar): \(z' = z + u * \tanh(w^T z + b)\)

  • Radial (radial): \(z' = z + \beta (\alpha + (z-z_r))^{-1}(z-z_r)\)

  • Householder (hh): \(z' = H z\)

Formula can be written as a string, e.g. ‘scale-loc’, ‘scale-hh*4-loc’, ‘panar*10’. Every step is separated with ‘-’, repeated flow is marked with ‘*’ producing ‘flow*repeats’.

Parameters
flow: str|AbstractFlow

formula or initialized Flow, default is ‘scale-loc’ that is identical to MeanField

model: :class:`pymc3.Model`

PyMC3 model for inference

random_seed: None or int

leave None to use package global RandomStream or other valid value to create instance specific one

class pymc3.variational.inference.SVGD(n_particles=100, jitter=1, model=None, start=None, random_seed=None, estimator=<class 'pymc3.variational.operators.KSD'>, kernel=<pymc3.variational.test_functions.RBF object>, **kwargs)

Stein Variational Gradient Descent

This inference is based on Kernelized Stein Discrepancy it’s main idea is to move initial noisy particles so that they fit target distribution best.

Algorithm is outlined below

Input: A target distribution with density function \(p(x)\)

and a set of initial particles \(\{x^0_i\}^n_{i=1}\)

Output: A set of particles \(\{x^{*}_i\}^n_{i=1}\) that approximates the target distribution.

\[\begin{split}x_i^{l+1} &\leftarrow x_i^{l} + \epsilon_l \hat{\phi}^{*}(x_i^l) \\ \hat{\phi}^{*}(x) &= \frac{1}{n}\sum^{n}_{j=1}[k(x^l_j,x) \nabla_{x^l_j} logp(x^l_j)+ \nabla_{x^l_j} k(x^l_j,x)]\end{split}\]
Parameters
n_particles: `int`

number of particles to use for approximation

jitter: `float`

noise sd for initial point

model: :class:`pymc3.Model`

PyMC3 model for inference

kernel: `callable`

kernel function for KSD \(f(histogram) -> (k(x,.), \nabla_x k(x,.))\)

temperature: float

parameter responsible for exploration, higher temperature gives more broad posterior estimate

start: `dict`

initial point for inference

random_seed: None or int

leave None to use package global RandomStream or other valid value to create instance specific one

start: `Point`

starting point for inference

kwargs: other keyword arguments passed to estimator

References

  • Qiang Liu, Dilin Wang (2016) Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm arXiv:1608.04471

  • Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng (2017) Stein Variational Policy Gradient arXiv:1704.02399

pymc3.variational.inference.fit(n=10000, local_rv=None, method='advi', model=None, random_seed=None, start=None, inf_kwargs=None, **kwargs)

Handy shortcut for using inference methods in functional way

Parameters
n: `int`

number of iterations

local_rv: dict[var->tuple]

mapping {model_variable -> approx params} Local Vars are used for Autoencoding Variational Bayes See (AEVB; Kingma and Welling, 2014) for details

method: str or :class:`Inference`

string name is case insensitive in:

  • ‘advi’ for ADVI

  • ‘fullrank_advi’ for FullRankADVI

  • ‘svgd’ for Stein Variational Gradient Descent

  • ‘asvgd’ for Amortized Stein Variational Gradient Descent

  • ‘nfvi’ for Normalizing Flow with default scale-loc flow

  • ‘nfvi=<formula>’ for Normalizing Flow using formula

model: :class:`Model`

PyMC3 model for inference

random_seed: None or int

leave None to use package global RandomStream or other valid value to create instance specific one

inf_kwargs: dict

additional kwargs passed to Inference

start: `Point`

starting point for inference

Returns
Approximation
Other Parameters
score: bool

evaluate loss on each iteration or not

callbacks: list[function: (Approximation, losses, i) -> None]

calls provided functions after each iteration step

progressbar: bool

whether to show progressbar or not

obj_n_mc: `int`

Number of monte carlo samples used for approximation of objective gradients

tf_n_mc: `int`

Number of monte carlo samples used for approximation of test function gradients

obj_optimizer: function (grads, params) -> updates

Optimizer that is used for objective params

test_optimizer: function (grads, params) -> updates

Optimizer that is used for test function params

more_obj_params: `list`

Add custom params for objective optimizer

more_tf_params: `list`

Add custom params for test function optimizer

more_updates: `dict`

Add custom updates to resulting updates

total_grad_norm_constraint: `float`

Bounds gradient norm, prevents exploding gradient problem

fn_kwargs: `dict`

Add kwargs to theano.function (e.g. {‘profile’: True})

more_replacements: `dict`

Apply custom replacements before calculating gradients

Approximations

class pymc3.variational.approximations.Empirical(trace=None, size=None, **kwargs)

Single Group Full Rank Approximation

Builds Approximation instance from a given trace, it has the same interface as variational approximation

evaluate_over_trace(node)

This allows to statically evaluate any symbolic expression over the trace.

Parameters
node: Theano Variables (or Theano expressions)
Returns
evaluated node(s) over the posterior trace contained in the empirical approximation
class pymc3.variational.approximations.FullRank(*args, **kwargs)

Single Group Full Rank Approximation

Full Rank approximation to the posterior where Multivariate Gaussian family is fitted to minimize KL divergence from True posterior. In contrast to MeanField approach correlations between variables are taken in account. The main drawback of the method is computational cost.

class pymc3.variational.approximations.MeanField(*args, **kwargs)

Single Group Mean Field Approximation

Mean Field approximation to the posterior where spherical Gaussian family is fitted to minimize KL divergence from True posterior. It is assumed that latent space variables are uncorrelated that is the main drawback of the method

class pymc3.variational.approximations.NormalizingFlow(flow='scale-loc', *args, **kwargs)

Single Group Normalizing Flow Approximation

Normalizing flow is a series of invertible transformations on initial distribution.

\[\begin{split}z_K &= f_K \circ \dots \circ f_2 \circ f_1(z_0) \\ & z_0 \sim \mathcal{N}(0, 1)\end{split}\]

In that case we can compute tractable density for the flow.

\[\ln q_K(z_K) = \ln q_0(z_0) - \sum_{k=1}^{K}\ln \left|\frac{\partial f_k}{\partial z_{k-1}}\right|\]

Every \(f_k\) here is a parametric function with defined determinant. We can choose every step here. For example the here is a simple flow is an affine transform:

\[z = loc(scale(z_0)) = \mu + \sigma * z_0\]

Here we get mean field approximation if \(z_0 \sim \mathcal{N}(0, 1)\)

Flow Formulas

In PyMC3 there is a flexible way to define flows with formulas. We have 5 of them by the moment:

  • Loc (loc): \(z' = z + \mu\)

  • Scale (scale): \(z' = \sigma * z\)

  • Planar (planar): \(z' = z + u * \tanh(w^T z + b)\)

  • Radial (radial): \(z' = z + \beta (\alpha + (z-z_r))^{-1}(z-z_r)\)

  • Householder (hh): \(z' = H z\)

Formula can be written as a string, e.g. ‘scale-loc’, ‘scale-hh*4-loc’, ‘panar*10’. Every step is separated with ‘-’, repeated flow is marked with ‘*’ producing ‘flow*repeats’.

References

  • Danilo Jimenez Rezende, Shakir Mohamed, 2015 Variational Inference with Normalizing Flows arXiv:1505.05770

  • Jakub M. Tomczak, Max Welling, 2016 Improving Variational Auto-Encoders using Householder Flow arXiv:1611.09630

pymc3.variational.approximations.sample_approx(approx, draws=100, include_transformed=True)

Draw samples from variational posterior.

Parameters
approx: :class:`Approximation`

Approximation to sample from

draws: `int`

Number of random samples.

include_transformed: `bool`

If True, transformed variables are also sampled. Default is True.

Returns
trace: class:pymc3.backends.base.MultiTrace

Samples drawn from variational posterior.

Operators

class pymc3.variational.operators.KL(approx, beta=1.0)

Operator based on Kullback Leibler Divergence

This operator constructs Evidence Lower Bound (ELBO) objective

\[ELBO_\beta = \log p(D|\theta) - \beta KL(q||p)\]

where

\[KL[q(v)||p(v)] = \int q(v)\log\frac{q(v)}{p(v)}dv\]
Parameters
approx: :class:`Approximation`

Approximation used for inference

beta: float

Beta parameter for KL divergence, scales the regularization term.

apply(f)

Operator itself

\[(O^{p,q}f_{\theta})(z)\]
Parameters
f: :class:`TestFunction` or None

function that takes z = self.input and returns same dimensional output

Returns
TensorVariable

symbolically applied operator

class pymc3.variational.operators.KSD(approx, temperature=1)

Operator based on Kernelized Stein Discrepancy

Input: A target distribution with density function \(p(x)\)

and a set of initial particles \(\{x^0_i\}^n_{i=1}\)

Output: A set of particles \(\{x_i\}^n_{i=1}\) that approximates the target distribution.

\[\begin{split}x_i^{l+1} \leftarrow \epsilon_l \hat{\phi}^{*}(x_i^l) \\ \hat{\phi}^{*}(x) = \frac{1}{n}\sum^{n}_{j=1}[k(x^l_j,x) \nabla_{x^l_j} logp(x^l_j)/temp + \nabla_{x^l_j} k(x^l_j,x)]\end{split}\]
Parameters
approx: :class:`Approximation`

Approximation used for inference

temperature: float

Temperature for Stein gradient

References

  • Qiang Liu, Dilin Wang (2016) Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm arXiv:1608.04471

apply(f)

Operator itself

\[(O^{p,q}f_{\theta})(z)\]
Parameters
f: :class:`TestFunction` or None

function that takes z = self.input and returns same dimensional output

Returns
TensorVariable

symbolically applied operator

objective_class

alias of pymc3.variational.operators.KSDObjective