Mixture¶

 Mixture(w, comp_dists, *args, **kwargs) Mixture log-likelihood NormalMixture(w, mu[, comp_shape]) Normal mixture log-likelihood
class pymc3.distributions.mixture.Mixture(w, comp_dists, *args, **kwargs)

Mixture log-likelihood

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 one-dimensional PyMC3 distributions the component distributions $$f_1, \ldots, f_n$$
class pymc3.distributions.mixture.NormalMixture(w, mu, comp_shape=(), *args, **kwargs)

Normal mixture log-likelihood

$f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i)$
 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