robust_shape_adapter: Create object to adapt proposal shape (and scale) using...

In rmcmc: Robust Markov Chain Monte Carlo Methods

Create object to adapt proposal shape (and scale) using robust adaptive Metropolis algorithm of Vihola (2012).

Description

Requires ramcmc package to be installed.

Usage

robust_shape_adapter(
  initial_scale = NULL,
  target_accept_prob = NULL,
  kappa = 0.6
)

Arguments

initial_scale	Initial value to use for scale parameter. If not set explicitly a proposal and dimension dependent default will be used.
target_accept_prob	Target value for average accept probability for chain. If not set a proposal dependent default will be used.
kappa	Decay rate exponent in ⁠[0.5, 1]⁠ for adaptation learning rate.

Value

List of functions with entries

• initialize, a function for initializing adapter state and proposal parameters at beginning of chain,

• update a function for updating adapter state and proposal parameters on each chain iteration,

• finalize a function for performing any final updates to adapter state and proposal parameters on completion of chain sampling (may be NULL if unused).

• state a zero-argument function for accessing current values of adapter state variables.

References

Vihola, M. (2012). Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22, 997-1008. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11222-011-9269-5")}

Examples

proposal <- barker_proposal()
adapter <- robust_shape_adapter(initial_scale = 1., target_accept_prob = 0.4)
adapter$initialize(proposal, chain_state(c(0, 0)))

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