# Continuous¶

 Uniform([lower, upper]) Continuous uniform log-likelihood. Flat(*args, **kwargs) Uninformative log-likelihood that returns 0 regardless of the passed value. HalfFlat(*args, **kwargs) Improper flat prior over the positive reals. Normal([mu, sd, tau]) Univariate normal log-likelihood. HalfNormal([sd, tau]) Half-normal log-likelihood. SkewNormal([mu, sd, tau, alpha]) Univariate skew-normal log-likelihood. Beta([alpha, beta, mu, sd]) Beta log-likelihood. Kumaraswamy(a, b, *args, **kwargs) Kumaraswamy log-likelihood. Exponential(lam, *args, **kwargs) Exponential log-likelihood. Laplace(mu, b, *args, **kwargs) Laplace log-likelihood. StudentT(nu[, mu, lam, sd]) Student’s T log-likelihood. HalfStudentT([nu, sd, lam]) Half Student’s T log-likelihood Cauchy(alpha, beta, *args, **kwargs) Cauchy log-likelihood. HalfCauchy(beta, *args, **kwargs) Half-Cauchy log-likelihood. Gamma([alpha, beta, mu, sd]) Gamma log-likelihood. Weibull(alpha, beta, *args, **kwargs) Weibull log-likelihood. Lognormal([mu, sd, tau]) Log-normal log-likelihood. ChiSquared(nu, *args, **kwargs) $$\chi^2$$ log-likelihood. Wald([mu, lam, phi, alpha]) Wald log-likelihood. Pareto(alpha, m[, transform]) Pareto log-likelihood. InverseGamma([alpha, beta, mu, sd]) Inverse gamma log-likelihood, the reciprocal of the gamma distribution. ExGaussian(mu, sigma, nu, *args, **kwargs) Exponentially modified Gaussian log-likelihood. VonMises([mu, kappa, transform]) Univariate VonMises log-likelihood. Triangular([lower, upper, c]) Continuous Triangular log-likelihood Gumbel([mu, beta]) Univariate Gumbel log-likelihood Logistic([mu, s]) Logistic log-likelihood. LogitNormal([mu, sd, tau]) Logit-Normal log-likelihood. Interpolated(x_points, pdf_points, *args, …) Univariate probability distribution defined as a linear interpolation of probability density function evaluated on some lattice of points.

A collection of common probability distributions for stochastic nodes in PyMC.

class pymc3.distributions.continuous.Uniform(lower=0, upper=1, *args, **kwargs)

Continuous uniform log-likelihood.

The pdf of this distribution is

$f(x \mid lower, upper) = \frac{1}{upper-lower}$
 Support $$x \in [lower, upper]$$ Mean $$\dfrac{lower + upper}{2}$$ Variance $$\dfrac{(upper - lower)^2}{12}$$
Parameters: lower (float) – Lower limit. upper (float) – Upper limit.
logp(value)

Calculate log-probability of Uniform distribution at specified value.

Parameters: value (numeric) – Value for which log-probability is calculated. TensorVariable
random(point=None, size=None)

Draw random values from Uniform distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Flat(*args, **kwargs)

Uninformative log-likelihood that returns 0 regardless of the passed value.

logp(value)

Calculate log-probability of Flat distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Raises ValueError as it is not possible to sample from Flat distribution

Parameters: point (dict, optional) – size (int, optional) – ValueError
class pymc3.distributions.continuous.HalfFlat(*args, **kwargs)

Improper flat prior over the positive reals.

logp(value)

Calculate log-probability of HalfFlat distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Raises ValueError as it is not possible to sample from HalfFlat distribution

Parameters: point (dict, optional) – size (int, optional) – ValueError
class pymc3.distributions.continuous.Normal(mu=0, sd=None, tau=None, **kwargs)

Univariate normal log-likelihood.

The pdf of this distribution is

$f(x \mid \mu, \tau) = \sqrt{\frac{\tau}{2\pi}} \exp\left\{ -\frac{\tau}{2} (x-\mu)^2 \right\}$

Normal distribution can be parameterized either in terms of precision or standard deviation. The link between the two parametrizations is given by

$\tau = \dfrac{1}{\sigma^2}$
 Support $$x \in \mathbb{R}$$ Mean $$\mu$$ Variance $$\dfrac{1}{\tau}$$ or $$\sigma^2$$
Parameters: mu (float) – Mean. sd (float) – Standard deviation (sd > 0) (only required if tau is not specified). tau (float) – Precision (tau > 0) (only required if sd is not specified).

Examples

with pm.Model():
x = pm.Normal('x', mu=0, sd=10)

with pm.Model():
x = pm.Normal('x', mu=0, tau=1/23)

logp(value)

Calculate log-probability of Normal distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Normal distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.TruncatedNormal(mu=0, sd=None, tau=None, lower=None, upper=None, transform='auto', *args, **kwargs)

Univariate truncated normal log-likelihood.

The pdf of this distribution is

$f(x;\mu ,\sigma ,a,b)={\frac {\phi ({\frac {x-\mu }{\sigma }})}{ \sigma \left(\Phi ({\frac {b-\mu }{\sigma }})-\Phi ({\frac {a-\mu }{\sigma }})\right)}}$

Truncated normal distribution can be parameterized either in terms of precision or standard deviation. The link between the two parametrizations is given by

$\tau = \dfrac{1}{\sigma^2}$
Parameters: mu (float) – Mean. sd (float) – Standard deviation (sd > 0). lower (float (optional)) – Left bound. upper (float (optional)) – Right bound.

Examples

with pm.Model():
x = pm.TruncatedNormal('x', mu=0, sd=10, lower=0)

with pm.Model():
x = pm.TruncatedNormal('x', mu=0, sd=10, upper=1)

with pm.Model():
x = pm.TruncatedNormal('x', mu=0, sd=10, lower=0, upper=1)

logp(value)

Calculate log-probability of TruncatedNormal distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from TruncatedNormal distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Beta(alpha=None, beta=None, mu=None, sd=None, *args, **kwargs)

Beta log-likelihood.

The pdf of this distribution is

$f(x \mid \alpha, \beta) = \frac{x^{\alpha - 1} (1 - x)^{\beta - 1}}{B(\alpha, \beta)}$
 Support $$x \in (0, 1)$$ Mean $$\dfrac{\alpha}{\alpha + \beta}$$ Variance $$\dfrac{\alpha \beta}{(\alpha+\beta)^2(\alpha+\beta+1)}$$

Beta distribution can be parameterized either in terms of alpha and beta or mean and standard deviation. The link between the two parametrizations is given by

\begin{align}\begin{aligned}\begin{split}\alpha &= \mu \kappa \\ \beta &= (1 - \mu) \kappa\end{split}\\\text{where } \kappa = \frac{\mu(1-\mu)}{\sigma^2} - 1\end{aligned}\end{align}
Parameters: alpha (float) – alpha > 0. beta (float) – beta > 0. mu (float) – Alternative mean (0 < mu < 1). sd (float) – Alternative standard deviation (0 < sd < sqrt(mu * (1 - mu))).

Notes

Beta distribution is a conjugate prior for the parameter $$p$$ of the binomial distribution.

logp(value)

Calculate log-probability of Beta distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Beta distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Kumaraswamy(a, b, *args, **kwargs)

Kumaraswamy log-likelihood.

The pdf of this distribution is

$f(x \mid a, b) = abx^{a-1}(1-x^a)^{b-1}$
 Support $$x \in (0, 1)$$ Mean $$b B(1 + \tfrac{1}{a}, b)$$ Variance $$b B(1 + \tfrac{2}{a}, b) - (b B(1 + \tfrac{1}{a}, b))^2$$
Parameters: a (float) – a > 0. b (float) – b > 0.
logp(value)

Calculate log-probability of Kumaraswamy distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Kumaraswamy distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Exponential(lam, *args, **kwargs)

Exponential log-likelihood.

The pdf of this distribution is

$f(x \mid \lambda) = \lambda \exp\left\{ -\lambda x \right\}$
 Support $$x \in [0, \infty)$$ Mean $$\dfrac{1}{\lambda}$$ Variance $$\dfrac{1}{\lambda^2}$$
Parameters: lam (float) – Rate or inverse scale (lam > 0)
logcdf(value)

Compute the log CDF for the Exponential distribution

References

 [Machler20125] Martin Mächler (2012). “Accurately computing log(1-exp(-|a|)) Assessed by the Rmpfr package”
logp(value)

Calculate log-probability of Exponential distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Exponential distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Laplace(mu, b, *args, **kwargs)

Laplace log-likelihood.

The pdf of this distribution is

$f(x \mid \mu, b) = \frac{1}{2b} \exp \left\{ - \frac{|x - \mu|}{b} \right\}$
 Support $$x \in \mathbb{R}$$ Mean $$\mu$$ Variance $$2 b^2$$
Parameters: mu (float) – Location parameter. b (float) – Scale parameter (b > 0).
logp(value)

Calculate log-probability of Laplace distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Laplace distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.StudentT(nu, mu=0, lam=None, sd=None, *args, **kwargs)

Student’s T log-likelihood.

Describes a normal variable whose precision is gamma distributed. If only nu parameter is passed, this specifies a standard (central) Student’s T.

The pdf of this distribution is

$f(x|\mu,\lambda,\nu) = \frac{\Gamma(\frac{\nu + 1}{2})}{\Gamma(\frac{\nu}{2})} \left(\frac{\lambda}{\pi\nu}\right)^{\frac{1}{2}} \left[1+\frac{\lambda(x-\mu)^2}{\nu}\right]^{-\frac{\nu+1}{2}}$
 Support $$x \in \mathbb{R}$$
Parameters: nu (float) – Degrees of freedom, also known as normality parameter (nu > 0). mu (float) – Location parameter. sd (float) – Scale parameter (sd > 0). Converges to the standard deviation as nu increases. (only required if lam is not specified) lam (float) – Scale parameter (lam > 0). Converges to the precision as nu increases. (only required if sd is not specified)

Examples

with pm.Model():
x = pm.StudentT('x', nu=15, mu=0, sd=10)

with pm.Model():
x = pm.StudentT('x', nu=15, mu=0, lam=1/23)

logp(value)

Calculate log-probability of StudentT distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from StudentT distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Cauchy(alpha, beta, *args, **kwargs)

Cauchy log-likelihood.

Also known as the Lorentz or the Breit-Wigner distribution.

The pdf of this distribution is

$f(x \mid \alpha, \beta) = \frac{1}{\pi \beta [1 + (\frac{x-\alpha}{\beta})^2]}$
 Support $$x \in \mathbb{R}$$ Mode $$\alpha$$ Mean undefined Variance undefined
Parameters: alpha (float) – Location parameter beta (float) – Scale parameter > 0
logp(value)

Calculate log-probability of Cauchy distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Cauchy distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.HalfCauchy(beta, *args, **kwargs)

Half-Cauchy log-likelihood.

The pdf of this distribution is

$f(x \mid \beta) = \frac{2}{\pi \beta [1 + (\frac{x}{\beta})^2]}$
 Support $$x \in [0, \infty)$$ Mode 0 Mean undefined Variance undefined
Parameters: beta (float) – Scale parameter (beta > 0).
logp(value)

Calculate log-probability of HalfCauchy distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from HalfCauchy distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Gamma(alpha=None, beta=None, mu=None, sd=None, *args, **kwargs)

Gamma log-likelihood.

Represents the sum of alpha exponentially distributed random variables, each of which has mean beta.

The pdf of this distribution is

$f(x \mid \alpha, \beta) = \frac{\beta^{\alpha}x^{\alpha-1}e^{-\beta x}}{\Gamma(\alpha)}$
 Support $$x \in (0, \infty)$$ Mean $$\dfrac{\alpha}{\beta}$$ Variance $$\dfrac{\alpha}{\beta^2}$$

Gamma distribution can be parameterized either in terms of alpha and beta or mean and standard deviation. The link between the two parametrizations is given by

$\begin{split}\alpha &= \frac{\mu^2}{\sigma^2} \\ \beta &= \frac{\mu}{\sigma^2}\end{split}$
Parameters: alpha (float) – Shape parameter (alpha > 0). beta (float) – Rate parameter (beta > 0). mu (float) – Alternative shape parameter (mu > 0). sd (float) – Alternative scale parameter (sd > 0).
logp(value)

Calculate log-probability of Gamma distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Gamma distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Weibull(alpha, beta, *args, **kwargs)

Weibull log-likelihood.

The pdf of this distribution is

$f(x \mid \alpha, \beta) = \frac{\alpha x^{\alpha - 1} \exp(-(\frac{x}{\beta})^{\alpha})}{\beta^\alpha}$
 Support $$x \in [0, \infty)$$ Mean $$\beta \Gamma(1 + \frac{1}{\alpha})$$ Variance $$\beta^2 \Gamma(1 + \frac{2}{\alpha} - \mu^2)$$
Parameters: alpha (float) – Shape parameter (alpha > 0). beta (float) – Scale parameter (beta > 0).
logcdf(value)

Compute the log CDF for the Weibull distribution

References

 [Machler20126] Martin Mächler (2012). “Accurately computing log(1-exp(-|a|)) Assessed by the Rmpfr package”
logp(value)

Calculate log-probability of Weibull distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Weibull distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.HalfStudentT(nu=1, sd=None, lam=None, *args, **kwargs)

Half Student’s T log-likelihood

The pdf of this distribution is

$f(x \mid \sigma,\nu) = \frac{2\;\Gamma\left(\frac{\nu+1}{2}\right)} {\Gamma\left(\frac{\nu}{2}\right)\sqrt{\nu\pi\sigma^2}} \left(1+\frac{1}{\nu}\frac{x^2}{\sigma^2}\right)^{-\frac{\nu+1}{2}}$
 Support $$x \in [0, \infty)$$
Parameters: nu (float) – Degrees of freedom, also known as normality parameter (nu > 0). sd (float) – Scale parameter (sd > 0). Converges to the standard deviation as nu increases. (only required if lam is not specified) lam (float) – Scale parameter (lam > 0). Converges to the precision as nu increases. (only required if sd is not specified)

Examples

# Only pass in one of lam or sd, but not both.
with pm.Model():
x = pm.HalfStudentT('x', sd=10, nu=10)

with pm.Model():
x = pm.HalfStudentT('x', lam=4, nu=10)

logp(value)

Calculate log-probability of HalfStudentT distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from HalfStudentT distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Lognormal(mu=0, sd=None, tau=None, *args, **kwargs)

Log-normal log-likelihood.

Distribution of any random variable whose logarithm is normally distributed. A variable might be modeled as log-normal if it can be thought of as the multiplicative product of many small independent factors.

The pdf of this distribution is

$f(x \mid \mu, \tau) = \frac{1}{x} \sqrt{\frac{\tau}{2\pi}} \exp\left\{ -\frac{\tau}{2} (\ln(x)-\mu)^2 \right\}$
 Support $$x \in [0, \infty)$$ Mean $$\exp\{\mu + \frac{1}{2\tau}\}$$ Variance $$(\exp\{\frac{1}{\tau}\} - 1) \times \exp\{2\mu + \frac{1}{\tau}\}$$
Parameters: mu (float) – Location parameter. sd (float) – Standard deviation. (sd > 0). (only required if tau is not specified). tau (float) – Scale parameter (tau > 0). (only required if sd is not specified).

Example

# Example to show that we pass in only sd or tau but not both.
with pm.Model():
x = pm.Lognormal('x', mu=2, sd=30)

with pm.Model():
x = pm.Lognormal('x', mu=2, tau=1/100)

logp(value)

Calculate log-probability of Lognormal distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Lognormal distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.ChiSquared(nu, *args, **kwargs)

$$\chi^2$$ log-likelihood.

The pdf of this distribution is

$f(x \mid \nu) = \frac{x^{(\nu-2)/2}e^{-x/2}}{2^{\nu/2}\Gamma(\nu/2)}$
 Support $$x \in [0, \infty)$$ Mean $$\nu$$ Variance $$2 \nu$$
Parameters: nu (int) – Degrees of freedom (nu > 0).
class pymc3.distributions.continuous.HalfNormal(sd=None, tau=None, *args, **kwargs)

Half-normal log-likelihood.

The pdf of this distribution is

\begin{align}\begin{aligned}f(x \mid \tau) = \sqrt{\frac{2\tau}{\pi}} \exp\left(\frac{-x^2 \tau}{2}\right)\\f(x \mid \sigma) =\sigma \sqrt{\frac{2}{\pi}} \exp\left(\frac{-x^2}{2\sigma^2}\right)\end{aligned}\end{align}

Note

The parameters sigma/tau ($$\sigma$$/$$\tau$$) refer to the standard deviation/precision of the unfolded normal distribution, for the standard deviation of the half-normal distribution, see below. For the half-normal, they are just two parameterisation $$\sigma^2 \equiv \frac{1}{\tau}$$ of a scale parameter

 Support $$x \in [0, \infty)$$ Mean $$\sqrt{\dfrac{2}{\tau \pi}}$$ or $$\dfrac{\sigma \sqrt{2}}{\sqrt{\pi}}$$ Variance $$\dfrac{1}{\tau}\left(1 - \dfrac{2}{\pi}\right)$$ or $$\sigma^2\left(1 - \dfrac{2}{\pi}\right)$$
Parameters: sd (float) – Scale parameter $$sigma$$ (sd > 0) (only required if tau is not specified). tau (float) – Precision $$tau$$ (tau > 0) (only required if sd is not specified).

Examples

with pm.Model():
x = pm.HalfNormal('x', sd=10)

with pm.Model():
x = pm.HalfNormal('x', tau=1/15)

logp(value)

Calculate log-probability of HalfNormal distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from HalfNormal distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Wald(mu=None, lam=None, phi=None, alpha=0.0, *args, **kwargs)

Wald log-likelihood.

The pdf of this distribution is

$f(x \mid \mu, \lambda) = \left(\frac{\lambda}{2\pi)}\right)^{1/2} x^{-3/2} \exp\left\{ -\frac{\lambda}{2x}\left(\frac{x-\mu}{\mu}\right)^2 \right\}$
 Support $$x \in (0, \infty)$$ Mean $$\mu$$ Variance $$\dfrac{\mu^3}{\lambda}$$

Wald distribution can be parameterized either in terms of lam or phi. The link between the two parametrizations is given by

$\phi = \dfrac{\lambda}{\mu}$
Parameters: mu (float, optional) – Mean of the distribution (mu > 0). lam (float, optional) – Relative precision (lam > 0). phi (float, optional) – Alternative shape parameter (phi > 0). alpha (float, optional) – Shift/location parameter (alpha >= 0).

Notes

To instantiate the distribution specify any of the following

• only mu (in this case lam will be 1)
• mu and lam
• mu and phi
• lam and phi

References

 [Tweedie19577] Tweedie, M. C. K. (1957). Statistical Properties of Inverse Gaussian Distributions I. The Annals of Mathematical Statistics, Vol. 28, No. 2, pp. 362-377
 [Michael19767] Michael, J. R., Schucany, W. R. and Hass, R. W. (1976). Generating Random Variates Using Transformations with Multiple Roots. The American Statistician, Vol. 30, No. 2, pp. 88-90
 [Giner20167] Göknur Giner, Gordon K. Smyth (2016) statmod: Probability Calculations for the Inverse Gaussian Distribution
logp(value)

Calculate log-probability of Wald distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Wald distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Pareto(alpha, m, transform='lowerbound', *args, **kwargs)

Pareto log-likelihood.

Often used to characterize wealth distribution, or other examples of the 80/20 rule.

The pdf of this distribution is

$f(x \mid \alpha, m) = \frac{\alpha m^{\alpha}}{x^{\alpha+1}}$
 Support $$x \in [m, \infty)$$ Mean $$\dfrac{\alpha m}{\alpha - 1}$$ for $$\alpha \ge 1$$ Variance $$\dfrac{m \alpha}{(\alpha - 1)^2 (\alpha - 2)}$$ for $$\alpha > 2$$
Parameters: alpha (float) – Shape parameter (alpha > 0). m (float) – Scale parameter (m > 0).
logp(value)

Calculate log-probability of Pareto distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Pareto distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.InverseGamma(alpha=None, beta=None, mu=None, sd=None, *args, **kwargs)

Inverse gamma log-likelihood, the reciprocal of the gamma distribution.

The pdf of this distribution is

$f(x \mid \alpha, \beta) = \frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{-\alpha - 1} \exp\left(\frac{-\beta}{x}\right)$
 Support $$x \in (0, \infty)$$ Mean $$\dfrac{\beta}{\alpha-1}$$ for $$\alpha > 1$$ Variance $$\dfrac{\beta^2}{(\alpha-1)^2(\alpha - 2)}$$ for $$\alpha > 2$$
Parameters: alpha (float) – Shape parameter (alpha > 0). beta (float) – Scale parameter (beta > 0). mu (float) – Alternative shape parameter (mu > 0). sd (float) – Alternative scale parameter (sd > 0).
logp(value)

Calculate log-probability of InverseGamma distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from InverseGamma distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.ExGaussian(mu, sigma, nu, *args, **kwargs)

Exponentially modified Gaussian log-likelihood.

Results from the convolution of a normal distribution with an exponential distribution.

The pdf of this distribution is

$f(x \mid \mu, \sigma, \tau) = \frac{1}{\nu}\; \exp\left\{\frac{\mu-x}{\nu}+\frac{\sigma^2}{2\nu^2}\right\} \Phi\left(\frac{x-\mu}{\sigma}-\frac{\sigma}{\nu}\right)$

where $$\Phi$$ is the cumulative distribution function of the standard normal distribution.

 Support $$x \in \mathbb{R}$$ Mean $$\mu + \nu$$ Variance $$\sigma^2 + \nu^2$$
Parameters: mu (float) – Mean of the normal distribution. sigma (float) – Standard deviation of the normal distribution (sigma > 0). nu (float) – Mean of the exponential distribution (nu > 0).

References

 [Rigby200510] Rigby R.A. and Stasinopoulos D.M. (2005). “Generalized additive models for location, scale and shape” Applied Statististics., 54, part 3, pp 507-554.
 [Lacouture200810] Lacouture, Y. and Couseanou, D. (2008). “How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times”. Tutorials in Quantitative Methods for Psychology, Vol. 4, No. 1, pp 35-45.
logcdf(value)

Compute the log CDF for the ExGaussian distribution

References

 [Rigby200512] R.A. Rigby (2005). “Generalized additive models for location, scale and shape” http://dx.doi.org/10.1111/j.1467-9876.2005.00510.x
logp(value)

Calculate log-probability of ExGaussian distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from ExGaussian distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.VonMises(mu=0.0, kappa=None, transform='circular', *args, **kwargs)

Univariate VonMises log-likelihood.

The pdf of this distribution is

$f(x \mid \mu, \kappa) = \frac{e^{\kappa\cos(x-\mu)}}{2\pi I_0(\kappa)}$

where $$I_0$$ is the modified Bessel function of order 0.

 Support $$x \in [-\pi, \pi]$$ Mean $$\mu$$ Variance $$1-\frac{I_1(\kappa)}{I_0(\kappa)}$$
Parameters: mu (float) – Mean. kappa (float) – Concentration (frac{1}{kappa} is analogous to sigma^2).
logp(value)

Calculate log-probability of VonMises distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from VonMises distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.SkewNormal(mu=0.0, sd=None, tau=None, alpha=1, *args, **kwargs)

Univariate skew-normal log-likelihood.

The pdf of this distribution is
$f(x \mid \mu, \tau, \alpha) = 2 \Phi((x-\mu)\sqrt{\tau}\alpha) \phi(x,\mu,\tau)$
 Support $$x \in \mathbb{R}$$ Mean $$\mu + \sigma \sqrt{\frac{2}{\pi}} \frac {\alpha }{{\sqrt {1+\alpha ^{2}}}}$$ Variance $$\sigma^2 \left( 1-\frac{2\alpha^2}{(\alpha^2+1) \pi} \right)$$

Skew-normal distribution can be parameterized either in terms of precision or standard deviation. The link between the two parametrizations is given by

$\tau = \dfrac{1}{\sigma^2}$
Parameters: mu (float) – Location parameter. sd (float) – Scale parameter (sd > 0). tau (float) – Alternative scale parameter (tau > 0). alpha (float) – Skewness parameter.

Notes

When alpha=0 we recover the Normal distribution and mu becomes the mean, tau the precision and sd the standard deviation. In the limit of alpha approaching plus/minus infinite we get a half-normal distribution.

logp(value)

Calculate log-probability of SkewNormal distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from SkewNormal distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Triangular(lower=0, upper=1, c=0.5, *args, **kwargs)

Continuous Triangular log-likelihood

The pdf of this distribution is

$\begin{split}\begin{cases} 0 & \text{for } x < a, \\ \frac{2(x-a)}{(b-a)(c-a)} & \text{for } a \le x < c, \\[4pt] \frac{2}{b-a} & \text{for } x = c, \\[4pt] \frac{2(b-x)}{(b-a)(b-c)} & \text{for } c < x \le b, \\[4pt] 0 & \text{for } b < x. \end{cases}\end{split}$
 Support $$x \in [lower, upper]$$ Mean $$\dfrac{lower + upper + c}{3}$$ Variance $$\dfrac{upper^2 + lower^2 +c^2 - lower*upper - lower*c - upper*c}{18}$$
Parameters: lower (float) – Lower limit. c (float) – mode upper (float) – Upper limit.
logp(value)

Calculate log-probability of Triangular distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Triangular distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Gumbel(mu=0, beta=1.0, **kwargs)
Univariate Gumbel log-likelihood

The pdf of this distribution is

$f(x \mid \mu, \beta) = -\frac{x - \mu}{\beta} - \exp \left(-\frac{x - \mu}{\beta} \right) - \log(\beta)$
 Support $$x \in \mathbb{R}$$ Mean $$\mu + \beta\gamma$$, where gamma is the Euler-Mascheroni constant Variance $$\frac{\pi^2}{6} \beta^2$$
Parameters: mu (float) – Location parameter. beta (float) – Scale parameter (beta > 0).
logp(value)

Calculate log-probability of Gumbel distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Gumbel distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Logistic(mu=0.0, s=1.0, *args, **kwargs)

Logistic log-likelihood.

The pdf of this distribution is

$f(x \mid \mu, s) = \frac{\exp\left(-\frac{x - \mu}{s}\right)}{s \left(1 + \exp\left(-\frac{x - \mu}{s}\right)\right)^2}$
 Support $$x \in \mathbb{R}$$ Mean $$\mu$$ Variance $$\frac{s^2 \pi^2}{3}$$
Parameters: mu (float) – Mean. s (float) – Scale (s > 0).
logcdf(value)

Compute the log CDF for the Logistic distribution

References

 [Machler201213] Martin Mächler (2012). “Accurately computing log(1-exp(-|a|)) Assessed by the Rmpfr package”
logp(value)

Calculate log-probability of Logistic distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Logistic distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.LogitNormal(mu=0, sd=None, tau=None, **kwargs)

Logit-Normal log-likelihood.

The pdf of this distribution is

$f(x \mid \mu, \tau) = \frac{1}{x(1-x)} \sqrt{\frac{\tau}{2\pi}} \exp\left\{ -\frac{\tau}{2} (logit(x)-\mu)^2 \right\}$
 Support $$x \in (0, 1)$$ Mean no analytical solution Variance no analytical solution
Parameters: mu (float) – Location parameter. sd (float) – Scale parameter (sd > 0). tau (float) – Scale parameter (tau > 0).
logp(value)

Calculate log-probability of LogitNormal distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from LogitNormal distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Interpolated(x_points, pdf_points, *args, **kwargs)

Univariate probability distribution defined as a linear interpolation of probability density function evaluated on some lattice of points.

The lattice can be uneven, so the steps between different points can have different size and it is possible to vary the precision between regions of the support.

The probability density function values don not have to be normalized, as the interpolated density is any way normalized to make the total probability equal to $1$.

Both parameters x_points and values pdf_points are not variables, but plain array-like objects, so they are constant and cannot be sampled.

 Support $$x \in [x\_points[0], x\_points[-1]]$$
Parameters: x_points (array-like) – A monotonically growing list of values pdf_points (array-like) – Probability density function evaluated on lattice x_points
logp(value)

Calculate log-probability of Interpolated distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(size=None)

Draw random values from Interpolated distribution.

Parameters: size (int, optional) – Desired size of random sample (returns one sample if not specified). array
class pymc3.distributions.continuous.Rice(nu=None, sd=None, *args, **kwargs)

Rice distribution.

$f(x\mid \nu ,\sigma )= {\frac {x}{\sigma ^{2}}}\exp \left({\frac {-(x^{2}+\nu ^{2})}{2\sigma ^{2}}}\right)I_{0}\left({\frac {x\nu }{\sigma ^{2}}}\right),$
 Support $$x \in (0, \infty)$$ Mean $$\sigma {\sqrt {\pi /2}}\,\,L_{{1/2}}(-\nu ^{2}/2\sigma ^{2})$$ Variance :math:2sigma ^{2}+nu ^{2}-{frac {pi sigma ^{2}}{2}}L_{{1/2}}^{2} left({frac {-nu ^{2}}{2sigma ^{2}}}right)
Parameters: nu (float) – shape parameter. sd (float) – standard deviation.
logp(value)

Calculate log-probability of Rice distribution at specified value.

Parameters: value (numeric) – Value(s) for which log-probability is calculated. If the log probabilities for multiple values are desired the values must be provided in a numpy array or theano tensor TensorVariable
random(point=None, size=None)

Draw random values from Rice distribution.

Parameters: point (dict, optional) – Dict of variable values on which random values are to be conditioned (uses default point if not specified). size (int, optional) – Desired size of random sample (returns one sample if not specified). array