target_distribution_from_stan_model: Construct target distribution from a BridgeStan 'StanModel'...

In rmcmc: Robust Markov Chain Monte Carlo Methods

Construct target distribution from a BridgeStan StanModel object.

Description

Construct target distribution from a BridgeStan StanModel object.

Usage

target_distribution_from_stan_model(
  model,
  include_log_density = TRUE,
  include_generated_quantities = FALSE,
  include_transformed_parameters = FALSE
)

Arguments

model	Stan model object to use for target (posterior) distribution.
include_log_density	Whether to include an entry log_density corresponding to current log density for target distribution in values returned by trace function.
include_generated_quantities	Whether to included generated quantities in Stan model definition in values returned by trace function.
include_transformed_parameters	Whether to include transformed parameters in Stan model definition in values returned by trace function.

Value

A list with entries

• log_density: A function to evaluate log density function for target distribution given current position vector.

• value_and_gradient_log_density: A function to evaluate value and gradient of log density function for target distribution given current position vector, returning as a list with entries value and gradient.

• trace_function: A function which given a chain_state() object returns a named vector of values to trace during sampling. The constrained parameter values of model will always be included.

Examples

model <- example_gaussian_stan_model()
target_distribution <- target_distribution_from_stan_model(model)
withr::with_seed(
  876287L, state <- chain_state(stats::rnorm(model$param_unc_num()))
)
state$log_density(target_distribution)
target_distribution$trace_function(state)

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