RWKernel

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RWKernel#

class liesel.goose.RWKernel(position_keys, initial_step_size=1.0, da_target_accept=0.234, da_gamma=0.05, da_kappa=0.75, da_t0=10, identifier='')[source]#

Bases: ModelMixin, TransitionMixin[RWKernelState, DefaultTransitionInfo], ReprMixin

A random walk kernel.

Uses Gaussian proposals, Metropolis-Hastings correction and dual averaging. Implements the Kernel protocol.

The kernel uses a default Metropolis-Hastings target acceptance probability of 0.234, which is optimal for a random walk sampler (in a certain sense). See Gelman et al. (1997) Weak convergence and optimal scaling of random walk Metropolis algorithms: https://doi.org/10.1214/aoap/1034625254.

Parameters:

  • position_keys (Sequence[str]) – Sequence of position keys (variable names) handled by this kernel.

  • initial_step_size (float, default: 1.0) – Value at which to start step size tuning.

  • da_target_accept (float, default: 0.234) – Target acceptance probability for dual averaging algorithm.

  • da_gamma (float, default: 0.05) – The adaptation regularization scale.

  • da_kappa (float, default: 0.75) – The adaptation relaxation exponent.

  • da_t0 (int, default: 10) – The adaptation iteration offset.

  • identifier (str, default: '') – An string acting as a unique identifier for this kernel.

Methods

end_epoch(prng_key, kernel_state, ...)

Sets the step size as found by the dual averaging algorithm.

end_warmup(prng_key, kernel_state, ...)

Currently does nothing.

init_state(prng_key, model_state)

Initializes the kernel state.

start_epoch(prng_key, kernel_state, ...)

Resets the state of the dual averaging algorithm.

tune(prng_key, kernel_state, model_state, epoch)

Currently does nothing.

Attributes

error_book

Dict of error codes and their meaning.

identifier

Kernel identifier, set by EngineBuilder

needs_history

Whether this kernel needs its history for tuning.

position_keys

Tuple of position keys handled by this kernel.
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