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

Source: R/adaptation.R
scale_adapter.Rd

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).

  • "adam" to use the Adam optimizer of Kingma and Ba (2014) applied to the acceptance-rate residual, following the implementation in the walnuts library.

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 the selected adapter constructor: see dual_averaging_scale_adapter(), stochastic_approximation_scale_adapter() or adam_scale_adapter() for the full list of parameters accepted by each. In practice, most users tuning the Adam adapter only need to adjust learning_rate; the moment-decay parameters beta_1, beta_2, epsilon and learn_rate_decay have sensible defaults that rarely need adjustment.

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.

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

See also

dual_averaging_scale_adapter(), stochastic_approximation_scale_adapter(), adam_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)))