API Reference

This reference provides detailed documentation for all modules, classes, and methods in the current release of PyMC experimental.

as_model(*model_args, **model_kwargs)	Decorator to provide context to PyMC models declared in a function.
MarginalModel(*args, **kwargs)	Subclass of PyMC Model that implements functionality for automatic marginalization of variables in the logp transformation
model_builder.ModelBuilder([model_config, ...])	ModelBuilder can be used to provide an easy-to-use API (similar to scikit-learn) for models and help with deployment.

Inference

fit(method, **kwargs)

Fit a model with an inference algorithm

Distributions

Chi(name, nu, **kwargs)	\(\chi\) log-likelihood.
Maxwell(name, a, **kwargs)	The Maxwell-Boltzmann distribution
DiscreteMarkovChain(*args[, steps, n_lags])	A Discrete Markov Chain is a sequence of random variables
GeneralizedPoisson(name, *args, **kwargs)	Generalized Poisson.
BetaNegativeBinomial(name, alpha, beta, r, ...)	Beta Negative Binomial distribution.
GenExtreme(name, *args[, rng, dims, ...])	Univariate Generalized Extreme Value log-likelihood
R2D2M2CP(name, output_sigma, input_sigma, *, ...)	R2D2M2CP Prior.
Skellam(name, mu1, mu2, **kwargs)	Skellam distribution.
histogram_approximation(name, dist, *, ...)	Approximate a distribution with a histogram potential.

Utils

spline.bspline_interpolation(x, *[, n, ...])	Interpolate sparse grid to dense grid using bsplines.
prior.prior_from_idata(idata[, name, var_names])	Create a prior from posterior using MvNormal approximation.

Statespace Models

• Statespace Core
• Kalman Filters
• Statespace Models
• Structural Components

Model Transforms

autoreparam.vip_reparametrize(model, var_names)	Repametrize Model using Variationally Informed Parametrization (VIP).
autoreparam.VIP(_logit_lambda)	Helper to reparemetrize VIP model.