API Reference#

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

marginal_model.MarginalModel(*args, **kwargs)

Subclass of PyMC Model that implements functionality for automatic marginalization of variables in the logp transformation

model_builder.ModelBuilder([data, ...])

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


GenExtreme(name, *args[, rng, dims, ...])

Univariate Generalized Extreme Value log-likelihood

GeneralizedPoisson(name, *args, **kwargs)

Generalized Poisson.

DiscreteMarkovChain(*args[, steps, n_lags])

A Discrete Markov Chain is a sequence of random variables

R2D2M2CP(name, output_sigma, input_sigma, *, ...)

R2D2M2CP Prior.

histogram_approximation(name, dist, *, ...)

Approximate a distribution with a histogram potential.



Clone a PyMC model.

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.


Convert Model to FunctionGraph.


Convert FunctionGraph to PyMC model.