random_walk_proposal: Create a new (Gaussian) random walk proposal object.

In rmcmc: Robust Markov Chain Monte Carlo Methods

Create a new (Gaussian) random walk proposal object.

Description

The Gaussian random walk proposal samples a new proposed state by perturbing the current state with zero-mean normally distributed noise.

Usage

random_walk_proposal(
  scale = NULL,
  shape = NULL,
  sample_auxiliary = stats::rnorm
)

Arguments

scale	Scale parameter of proposal distribution. A non-negative scalar value determining scale of steps proposed.
shape	Shape parameter of proposal distribution. Either a vector corresponding to a diagonal shape matrix with per-dimension scaling factors, or a matrix allowing arbitrary linear transformations.
sample_auxiliary	Function which generates a random vector from auxiliary variable distribution.

Value

Proposal object. A list with entries

• sample: a function to generate sample from proposal distribution given current chain state,

• log_density_ratio: a function to compute log density ratio for proposal for a given pair of current and proposed chain states,

• update: a function to update parameters of proposal,

• parameters: a function to return list of current parameter values.

• default_target_accept_prob: a function returning the default target acceptance rate to use for any scale adaptation.

• default_initial_scale: a function which given a dimension gives a default value to use for the initial proposal scale parameter.

Examples

target_distribution <- list(log_density = function(x) -sum(x^2) / 2)
proposal <- random_walk_proposal(scale = 1.)
state <- chain_state(c(0., 0.))
withr::with_seed(
  876287L, proposed_state <- proposal$sample(state, target_distribution)
)
log_density_ratio <- proposal$log_density_ratio(
  state, proposed_state, target_distribution
)
proposal$update(scale = 0.5)

rmcmc documentation built on April 3, 2025, 5:27 p.m.