dual_averaging_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 using dual averaging scheme of Nesterov (2009) and Hoffman and Gelman (2014).

Description

Create object to adapt proposal scale to coerce average acceptance rate using dual averaging scheme of Nesterov (2009) and Hoffman and Gelman (2014).

Usage

dual_averaging_scale_adapter(
  initial_scale = NULL,
  target_accept_prob = NULL,
  kappa = 0.75,
  gamma = 0.05,
  iteration_offset = 10,
  mu = NULL
)

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. Defaults to value recommended in Hoffman and Gelman (2014).
gamma	Regularization coefficient for (log) scale in dual averaging algorithm. Controls amount of regularization of (log) scale towards mu. Should be set to a non-negative value. Defaults to value recommended in Hoffman and Gelman (2014).
iteration_offset	Offset to chain iteration used for the iteration based weighting of the adaptation statistic error estimate. Should be set to a non-negative value. A value greater than zero has the effect of stabilizing early iterations. Defaults to value recommended in Hoffman and Gelman (2014).
mu	Value to regularize (log) scale towards. If NULL (the default), mu will be set to log(10 * initial_scale), as recommended in Hoffman and Gelman (2014).

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.

Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593-1623.

Examples

proposal <- barker_proposal()
adapter <- dual_averaging_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.