RWKernel

class liesel.goose.rw.RWKernel(position_keys, initial_step_size=1.0, da_target_accept=0.234, da_gamma=0.05, da_kappa=0.75, da_t0=10)

Bases: liesel.goose.kernel.ModelMixin, liesel.goose.kernel.TransitionMixin[liesel.goose.rw.RWKernelState, liesel.goose.kernel.DefaultTransitionInfo]

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.

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.
has_model()	Whether the model interface is set.
init_state(prng_key, model_state)	Initializes the kernel state.
log_prob_fn(model_state)	Returns the log-probability function with the position as the only argument.
position(model_state)	Extracts the position from a model state.
set_model(model)	Sets the model interface.
start_epoch(prng_key, kernel_state, ...)	Resets the state of the dual averaging algorithm.
transition(prng_key, kernel_state, ...)	Handles one transition.
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
model	Returns the model interface if it is set.
needs_history	Whether this kernel needs its history for tuning.
position_keys	Tuple of position keys handled by this kernel.