samplers: MCMC samplers

In greta: Simple and Scalable Statistical Modelling in R

MCMC samplers

Description

Functions to set up MCMC samplers and change the starting values of their parameters, for use in mcmc().

Usage

hmc(Lmin = 5, Lmax = 10, epsilon = 0.1, diag_sd = 1)

rwmh(proposal = c("normal", "uniform"), epsilon = 0.1, diag_sd = 1)

slice(max_doublings = 5)

Arguments

Lmin	minimum number of leapfrog steps (positive integer, Lmin > Lmax)
Lmax	maximum number of leapfrog steps (positive integer, Lmax > Lmin)
epsilon	leapfrog stepsize hyperparameter (positive, will be tuned)
diag_sd	estimate of the posterior marginal standard deviations (positive, will be tuned).
proposal	the probability distribution used to generate proposal states
max_doublings	the maximum number of iterations of the 'doubling' algorithm used to adapt the size of the slice

Details

During the warmup iterations of mcmc, some of these sampler parameters will be tuned to improve the efficiency of the sampler, so the values provided here are used as starting values.

For hmc(), the number of leapfrog steps at each iteration is selected uniformly at random from between Lmin and Lmax. diag_sd is used to rescale the parameter space to make it more uniform, and make sampling more efficient.

rwmh() creates a random walk Metropolis-Hastings sampler; a a gradient-free sampling algorithm. The algorithm involves a proposal generating step proposal_state = current_state + perturb by a random perturbation, followed by Metropolis-Hastings accept/reject step. The class is implemented for uniform and normal proposals.

slice() implements a multivariate slice sampling algorithm. The parameter max_doublings is not tuned during warmup.

Value

a sampler object that can be passed to mcmc().

greta documentation built on April 4, 2025, 12:37 a.m.