mcmc_uncalibrated_hamiltonian_monte_carlo: Runs one step of Uncalibrated Hamiltonian Monte Carlo

In tfprobability: Interface to 'TensorFlow Probability'

Runs one step of Uncalibrated Hamiltonian Monte Carlo

Description

Warning: this kernel will not result in a chain which converges to the target_log_prob. To get a convergent MCMC, use mcmc_hamiltonian_monte_carlo(...) or mcmc_metropolis_hastings(mcmc_uncalibrated_hamiltonian_monte_carlo(...)). For more details on UncalibratedHamiltonianMonteCarlo, see HamiltonianMonteCarlo.

Usage

mcmc_uncalibrated_hamiltonian_monte_carlo(
  target_log_prob_fn,
  step_size,
  num_leapfrog_steps,
  state_gradients_are_stopped = FALSE,
  seed = NULL,
  store_parameters_in_results = FALSE,
  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.
step_size	Tensor or list of Tensors representing the step size for the leapfrog integrator. Must broadcast with the shape of current_state. Larger step sizes lead to faster progress, but too-large step sizes make rejection exponentially more likely. When possible, it's often helpful to match per-variable step sizes to the standard deviations of the target distribution in each variable.
num_leapfrog_steps	Integer number of steps to run the leapfrog integrator for. Total progress per HMC step is roughly proportional to step_size * num_leapfrog_steps.
state_gradients_are_stopped	logical indicating that the proposed new state be run through tf$stop_gradient. This is particularly useful when combining optimization over samples from the HMC chain. Default value: FALSE (i.e., do not apply stop_gradient).
seed	integer to seed the random number generator.
store_parameters_in_results	If TRUE, then step_size and num_leapfrog_steps are written to and read from eponymous fields in the kernel results objects returned from one_step and bootstrap_results. This allows wrapper kernels to adjust those parameters on the fly. This is incompatible with step_size_update_fn, which must be set to NULL.
name	string prefixed to Ops created by this function. Default value: NULL (i.e., 'hmc_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_langevin(), mcmc_uncalibrated_random_walk()

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