Runs one step of Uncalibrated Langevin discretized diffusion.

The class generates a Langevin proposal using _euler_method function and also computes helper UncalibratedLangevinKernelResults for the next iteration. Warning: this kernel will not result in a chain which converges to the target_log_prob. To get a convergent MCMC, use MetropolisAdjustedLangevinAlgorithm(...) or MetropolisHastings(UncalibratedLangevin(...)).

mcmc_uncalibrated_langevin(
  target_log_prob_fn,
  step_size,
  volatility_fn = NULL,
  parallel_iterations = 10,
  compute_acceptance = TRUE,
  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.
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.
volatility_fn	function which takes an argument like current_state (or *current_state if it's a list) and returns volatility value at current_state. Should return a Tensor or list of Tensors that must broadcast with the shape of current_state. Defaults to the identity function.
parallel_iterations	the number of coordinates for which the gradients of the volatility matrix volatility_fn can be computed in parallel.
compute_acceptance	logical indicating whether to compute the Metropolis log-acceptance ratio used to construct MetropolisAdjustedLangevinAlgorithm kernel.
seed	integer to seed the random number generator.
name	String prefixed to Ops created by this function. Default value: NULL (i.e., 'mala_kernel').

Value

list of next_state (Tensor or Python list of Tensors representing the state(s) of the Markov chain(s) at each result step. Has same shape as and current_state.) and kernel_results (collections$namedtuple of internal calculations used to 'advance the chain).

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_random_walk()