Mixture¶
Mixture (w, comp_dists, *args, **kwargs) 
Mixture loglikelihood 
NormalMixture (w, mu[, comp_shape]) 
Normal mixture loglikelihood 

class
pymc3.distributions.mixture.
Mixture
(w, comp_dists, *args, **kwargs)¶ Mixture loglikelihood
Often used to model subpopulation heterogeneity
\[f(x \mid w, \theta) = \sum_{i = 1}^n w_i f_i(x \mid \theta_i)\]Support \(\cap_{i = 1}^n \textrm{support}(f_i)\) Mean \(\sum_{i = 1}^n w_i \mu_i\) Parameters: w : array of floats
w >= 0 and w <= 1 the mixture weights
comp_dists : multidimensional PyMC3 distribution (e.g. pm.Poisson.dist(…))
or iterable of onedimensional PyMC3 distributions the component distributions \(f_1, \ldots, f_n\)

class
pymc3.distributions.mixture.
NormalMixture
(w, mu, comp_shape=(), *args, **kwargs)¶ Normal mixture loglikelihood
\[f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i)\]Support \(x \in \mathbb{R}\) Mean \(\sum_{i = 1}^n w_i \mu_i\) Variance \(\sum_{i = 1}^n w_i^2 \sigma^2_i\) Parameters: w : array of floats
w >= 0 and w <= 1 the mixture weights
mu : array of floats
the component means
sd : array of floats
the component standard deviations
tau : array of floats
the component precisions
comp_shape : shape of the Normal component
notice that it should be different than the shape of the mixture distribution, with one axis being the number of components.
Note: You only have to pass in sd or tau, but not both.

pymc3.distributions.mixture.
all_discrete
(comp_dists)¶ Determine if all distributions in comp_dists are discrete