blackjax.smc.adaptive_tempered

blackjax.smc.adaptive_tempered#

Attributes#

init

Functions#

build_kernel(→ Callable)

Build a Tempered SMC step using an adaptive schedule.

as_top_level_api(→ blackjax.base.SamplingAlgorithm)

Implements the user interface for the Adaptive Tempered SMC kernel.

Module Contents#

build_kernel(logprior_fn: Callable, loglikelihood_fn: Callable, mcmc_step_fn: Callable, mcmc_init_fn: Callable, resampling_fn: Callable, target_ess: float, root_solver: Callable = solver.dichotomy, **extra_parameters: dict[str, Any]) → Callable[source]#

Build a Tempered SMC step using an adaptive schedule.

Parameters:

  • logprior_fn (Callable) – Log prior probability function.

  • loglikelihood_fn (Callable) – Log likelihood function.

  • mcmc_step_fn (Callable) – Function that creates MCMC step from log-probability density function.

  • mcmc_init_fn (Callable) – A function that creates a new mcmc state from a position and a log-probability density function.

  • resampling_fn (Callable) – Resampling function (from blackjax.smc.resampling).

  • target_ess (float | Array) – Target effective sample size (ESS) to determine the next tempering parameter.

  • root_solver (Callable, optional) – The solver used to adaptively compute the temperature given a target number of effective samples. By default, blackjax.smc.solver.dichotomy.

  • **extra_parameters (dict[str, Any]) – Additional parameters to pass to tempered.build_kernel.

Returns:

kernel – A callable that takes a rng_key, a TemperedSMCState, num_mcmc_steps, and mcmc_parameters, and returns a new TemperedSMCState along with information about the transition.

Return type:

Callable

init[source]#
as_top_level_api(logprior_fn: Callable, loglikelihood_fn: Callable, mcmc_step_fn: Callable, mcmc_init_fn: Callable, mcmc_parameters: dict, resampling_fn: Callable, target_ess: float, root_solver: Callable = solver.dichotomy, num_mcmc_steps: int = 10, **extra_parameters: dict[str, Any]) → blackjax.base.SamplingAlgorithm[source]#

Implements the user interface for the Adaptive Tempered SMC kernel.

Parameters:

  • logprior_fn (Callable) – The log-prior function of the model we wish to draw samples from.

  • loglikelihood_fn (Callable) – The log-likelihood function of the model we wish to draw samples from.

  • mcmc_step_fn (Callable) – The MCMC step function used to update the particles.

  • mcmc_init_fn (Callable) – The MCMC init function used to build a MCMC state from a particle position.

  • mcmc_parameters (dict) – The parameters of the MCMC step function. Parameters with leading dimension length of 1 are shared amongst the particles.

  • resampling_fn (Callable) – The function used to resample the particles.

  • target_ess (float | Array) – Target effective sample size (ESS) to determine the next tempering parameter.

  • root_solver (Callable, optional) – The solver used to adaptively compute the temperature given a target number of effective samples. By default, blackjax.smc.solver.dichotomy.

  • num_mcmc_steps (int, optional) – The number of times the MCMC kernel is applied to the particles per step, by default 10.

  • **extra_parameters (dict [str, Any]) – Additional parameters to pass to the kernel.

Returns:

A SamplingAlgorithm instance with init and step methods.

Return type:

SamplingAlgorithm

Contents