mcmc_random_walk_metropolis | R Documentation |
Random Walk Metropolis is a gradient-free Markov chain Monte Carlo
(MCMC) algorithm. The algorithm involves a proposal generating step
proposal_state = current_state + perturb
by a random
perturbation, followed by Metropolis-Hastings accept/reject step. For more
details see Section 2.1 of Roberts and Rosenthal (2004).
mcmc_random_walk_metropolis( target_log_prob_fn, new_state_fn = NULL, seed = NULL, name = NULL )
target_log_prob_fn |
Function which takes an argument like
|
new_state_fn |
Function which takes a list of state parts and a
seed; returns a same-type |
seed |
integer to seed the random number generator. |
name |
String name prefixed to Ops created by this function.
Default value: |
The current class implements RWM for normal and uniform proposals. Alternatively,
the user can supply any custom proposal generating function.
The function one_step
can update multiple chains in parallel. It assumes
that all leftmost dimensions of current_state
index independent chain states
(and are therefore updated independently). The output of
target_log_prob_fn(current_state)
should sum log-probabilities across all
event dimensions. Slices along the rightmost dimensions may have different
target distributions; for example, current_state[0, :]
could have a
different target distribution from current_state[1, :]
. These semantics
are governed by target_log_prob_fn(current_state)
. (The number of
independent chains is tf$size(target_log_prob_fn(current_state))
.)
a Monte Carlo sampling 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_replica_exchange_mc()
,
mcmc_simple_step_size_adaptation()
,
mcmc_slice_sampler()
,
mcmc_transformed_transition_kernel()
,
mcmc_uncalibrated_hamiltonian_monte_carlo()
,
mcmc_uncalibrated_langevin()
,
mcmc_uncalibrated_random_walk()
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