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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 TransitionKernels.
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.