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

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. https://doi.iorg/10.1007/s11222-011-9269-5

Examples

target_distribution <- list(
  log_density = function(x) -sum(x^2) / 2,
  grad_log_density = function(x) -x
)
proposal <- barker_proposal(target_distribution)
adapter <- robust_shape_adapter(initial_scale = 1., target_accept_prob = 0.4)
adapter$initialize(proposal, chain_state(c(0, 0)))