tfprobability: mcmc_uncalibrated_langevin – R documentation

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mcmc_uncalibrated_langevin

Runs one step of Uncalibrated Langevin discretized diffusion.

Description

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(...)).

Usage

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 `Tensor`s 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 `Tensor`s 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).

mcmc_uncalibrated_langevin

Description

Usage

Arguments

Value

See Also

tfprobability

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