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.


fit(method, **kwargs)

Fit a model with an inference algorithm


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.


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#

Model Transforms#

autoreparam.vip_reparametrize(model, var_names)

Repametrize Model using Variationally Informed Parametrization (VIP).


Helper to reparemetrize VIP model.