blackjax.smc.tempered

blackjax.smc.tempered#

Classes#

TemperedSMCState

Current state for the tempered SMC algorithm.

Functions#

init(particles)

build_kernel(→ Callable)

Build the base Tempered SMC kernel.

as_top_level_api(→ blackjax.base.SamplingAlgorithm)

Implements the (basic) user interface for the Adaptive Tempered SMC kernel.

Module Contents#

class TemperedSMCState[source]#

Current state for the tempered SMC algorithm.

particles: PyTree

The particles’ positions.

lmbda: float

Current value of the tempering parameter.

particles: blackjax.types.ArrayTree[source]#
weights: blackjax.types.Array[source]#
lmbda: float[source]#
init(particles: blackjax.types.ArrayLikeTree)[source]#
build_kernel(logprior_fn: Callable, loglikelihood_fn: Callable, mcmc_step_fn: Callable, mcmc_init_fn: Callable, resampling_fn: Callable, update_strategy: Callable = update_and_take_last) → Callable[source]#

Build the base Tempered SMC kernel.

Tempered SMC uses tempering to sample from a distribution given by

\[p(x) \propto p_0(x) \exp(-V(x)) \mathrm{d}x\]

where \(p_0\) is the prior distribution, typically easy to sample from and for which the density is easy to compute, and \(\exp(-V(x))\) is an unnormalized likelihood term for which \(V(x)\) is easy to compute pointwise.

Parameters:

  • logprior_fn – A function that computes the log density of the prior distribution

  • loglikelihood_fn – A function that returns the probability at a given position.

  • mcmc_step_fn – A function that creates a mcmc kernel from a 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 – A random function that resamples generated particles based of weights

  • num_mcmc_iterations – Number of iterations in the MCMC chain.

Returns:

  • A callable that takes a rng_key and a TemperedSMCState that contains the current state

  • of the chain and that returns a new state of the chain along with

  • information about the transition.

as_top_level_api(logprior_fn: Callable, loglikelihood_fn: Callable, mcmc_step_fn: Callable, mcmc_init_fn: Callable, mcmc_parameters: dict, resampling_fn: Callable, num_mcmc_steps: int | None = 10, update_strategy=update_and_take_last) → blackjax.base.SamplingAlgorithm[source]#

Implements the (basic) user interface for the Adaptive Tempered SMC kernel.

Parameters:

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

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

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

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

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

  • resampling_fn – The function used to resample the particles.

  • num_mcmc_steps – The number of times the MCMC kernel is applied to the particles per step.

Return type:

A SamplingAlgorithm.