Distributions

Distributions that are not (or not yet) part of PyMC itself. They behave like regular PyMC distributions and can be used directly inside a model.

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.

Transforms

Value transforms for constrained sampling.

PartialOrder(adj_mat)

Create a PartialOrder transform