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Module: tfp.mcmc

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 HamiltonianMonteCarlo`: Runs one step of Hamiltonian Monte Carlo.

`class MetropolisAdjustedLangevinAlgorithm`: Runs one step of Metropolis-adjusted Langevin algorithm.

`class MetropolisHastings`: Runs one step of the Metropolis-Hastings algorithm.

`class NoUTurnSampler`: Runs one step of the No U-Turn Sampler.

`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 UncalibratedHamiltonianMonteCarlo`: Runs one step of Uncalibrated Hamiltonian Monte Carlo.

`class UncalibratedLangevin`: Runs one step of Uncalibrated Langevin discretized diffusion.

`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.

`make_simple_step_size_update_policy(...)`: Create a function implementing a step-size update policy. (deprecated)

`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.

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