scale_adapter: Create object to adapt proposal scale to coerce average...

In rmcmc: Robust Markov Chain Monte Carlo Methods

Create object to adapt proposal scale to coerce average acceptance rate.

Description

Create object to adapt proposal scale to coerce average acceptance rate.

Usage

scale_adapter(
  algorithm = "dual_averaging",
  initial_scale = NULL,
  target_accept_prob = NULL,
  ...
)

Arguments

algorithm	String specifying algorithm to use. One of: "stochastic_approximation" to use a Robbins-Monro (1951) based scheme, "dual_averaging" to use dual-averaging scheme of Nesterov (2009).
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.
...	Any additional algorithmic parameters to pass through to dual_averaging_scale_adapter() or stochastic_approximation_scale_adapter().

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

Nesterov, Y. (2009). Primal-dual subgradient methods for convex problems. Mathematical Programming, 120(1), 221-259.

Robbins, H., & Monro, S. (1951). A stochastic approximation method. The Annals of Mathematical Statistics, 400-407.

See Also

dual_averaging_scale_adapter(), stochastic_approximation_scale_adapter()

Examples

proposal <- barker_proposal()
adapter <- scale_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.