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(...)).
mcmc_uncalibrated_random_walk(
target_log_prob_fn,
new_state_fn = NULL,
seed = NULL,
name = NULL
)a Monte Carlo sampling kernel
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.
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().
integer to seed the random number generator.
String name prefixed to Ops created by this function.
Default value: NULL (i.e., 'rwm_kernel').
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()