oryx.experimental.mcmc.metropolis_hastings

Returns a program that takes a Metropolis-Hastings step.

View aliases

Main aliases

oryx.experimental.mcmc.kernels.metropolis_hastings

oryx.experimental.mcmc.metropolis_hastings(
    unnormalized_log_prob: oryx.core.ppl.LogProbFunction,
    inner_step: oryx.core.ppl.LogProbFunction
) -> oryx.core.ppl.LogProbFunction

The Metropolis-Hastings algorithm takes a proposal distribution (inner_step) and iteratively accepts or rejects proposals from it according to an accept ratio calculated from unnormalized_log_prob and log_prob(inner_step). This creates a Markov Chain whose stationary distribution is some target distribution (specified by unnormalized_log_prob).

Args
unnormalized_log_prob	A function that computes the log probability of a state.
inner_step	A probabilistic program that acts as the proposal distribution for a Metropolis-Hasting step.

Returns
A program that proposes a new state and accepts or rejects according to the unnormalized log probability and proposal distribution transition probabilities.