Mixture¶
Mixture (w, comp_dists, *args, **kwargs) |
Mixture log-likelihood |
NormalMixture (w, mu, *args, **kwargs) |
Normal mixture log-likelihood |
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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 or iterable of one-dimensional PyMC3 distributions) – the component distributions \(f_1, \ldots, f_n\)
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class
pymc3.distributions.mixture.
NormalMixture
(w, mu, *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
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pymc3.distributions.mixture.
all_discrete
(comp_dists)¶ Determine if all distributions in comp_dists are discrete