mcmc_uncalibrated_random_walk: Generate proposal for the Random Walk Metropolis algorithm.

In tfprobability: Interface to 'TensorFlow Probability'

Generate proposal for the Random Walk Metropolis algorithm.

Description

Warning: this kernel will not result in a chain which converges to the target_log_prob. To get a convergent MCMC, use mcmc_random_walk_metropolis(...) or mcmc_metropolis_hastings(mcmc_uncalibrated_random_walk(...)).

Usage

mcmc_uncalibrated_random_walk(
  target_log_prob_fn,
  new_state_fn = NULL,
  seed = NULL,
  name = NULL
)

Arguments

target_log_prob_fn	Function which takes an argument like current_state ((if it's a list current_state will be unpacked) and returns its (possibly unnormalized) log-density under the target distribution.
new_state_fn	Function which takes a list of state parts and a seed; returns a same-type list of Tensors, each being a perturbation of the input state parts. The perturbation distribution is assumed to be a symmetric distribution centered at the input state part. Default value: NULL which is mapped to tfp$mcmc$random_walk_normal_fn().
seed	integer to seed the random number generator.
name	String name prefixed to Ops created by this function. Default value: NULL (i.e., 'rwm_kernel').

Value

a Monte Carlo sampling kernel

See Also

Other mcmc_kernels: mcmc_dual_averaging_step_size_adaptation(), mcmc_hamiltonian_monte_carlo(), mcmc_metropolis_adjusted_langevin_algorithm(), mcmc_metropolis_hastings(), mcmc_no_u_turn_sampler(), mcmc_random_walk_metropolis(), mcmc_replica_exchange_mc(), mcmc_simple_step_size_adaptation(), mcmc_slice_sampler(), mcmc_transformed_transition_kernel(), mcmc_uncalibrated_hamiltonian_monte_carlo(), mcmc_uncalibrated_langevin()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.