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TensorFlow Probability MCMC python package.
Classes
class CheckpointableStatesAndTrace
: States and auxiliary trace of an MCMC chain.
class DualAveragingStepSizeAdaptation
: Adapts the inner kernel's step_size
based on log_accept_prob
.
class MetropolisHastings
: Runs one step of the Metropolis-Hastings algorithm.
class RandomWalkMetropolis
: Runs one step of the RWM algorithm with symmetric proposal.
class ReplicaExchangeMC
: Runs one step of the Replica Exchange Monte Carlo.
class SimpleStepSizeAdaptation
: Adapts the inner kernel's step_size
based on log_accept_prob
.
class SliceSampler
: Runs one step of the slice sampler using a hit and run approach.
class StatesAndTrace
: States and auxiliary trace of an MCMC chain.
class TransformedTransitionKernel
: TransformedTransitionKernel applies a bijector to the MCMC's state space.
class TransitionKernel
: Base class for all MCMC TransitionKernel
s.
class UncalibratedRandomWalk
: Generate proposal for the Random Walk Metropolis algorithm.
Functions
default_swap_proposal_fn(...)
: Make the default swap proposal func, with P[swap]
, for replica swap MC.
effective_sample_size(...)
: Estimate a lower bound on effective sample size for each independent chain.
even_odd_swap_proposal_fn(...)
: Make a deterministic swap proposal function, alternating even/odd swaps.
potential_scale_reduction(...)
: Gelman and Rubin (1992)'s potential scale reduction for chain convergence.
random_walk_normal_fn(...)
: Returns a callable that adds a random normal perturbation to the input.
random_walk_uniform_fn(...)
: Returns a callable that adds a random uniform perturbation to the input.
sample_annealed_importance_chain(...)
: Runs annealed importance sampling (AIS) to estimate normalizing constants.
sample_chain(...)
: Implements Markov chain Monte Carlo via repeated TransitionKernel
steps.
sample_halton_sequence(...)
: Returns a sample from the dim
dimensional Halton sequence.