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TensorFlow Probability experimental MCMC package.
Classes
class CovarianceReducer: Reducer that computes a running covariance.
class DiagonalMassMatrixAdaptation: Adapts the inner kernel's momentum_distribution to estimated variance.
class EllipticalSliceSampler: Runs one step of the elliptic slice sampler.
class ExpectationsReducer: Reducer that computes a running expectation.
class GradientBasedTrajectoryLengthAdaptation: Use gradient ascent to adapt inner kernel's trajectory length.
class GradientBasedTrajectoryLengthAdaptationResults: Internal state of GradientBasedTrajectoryLengthAdaptation.
class KernelBuilder: Convenience constructor for common MCMC transition kernels.
class KernelOutputs: Facade around outputs of step_kernel.
class NoUTurnSampler: Runs one step of the No U-Turn Sampler.
class PotentialScaleReductionReducer: Reducer that computes a running R-hat diagnostic statistic.
class PreconditionedHamiltonianMonteCarlo: Hamiltonian Monte Carlo, with given momentum distribution.
class PreconditionedNoUTurnSampler: Runs one step of the No U-Turn Sampler.
class ProgressBarReducer: Reducer that displays a progress bar.
class Reducer: Base class for all MCMC Reducers.
class SampleDiscardingKernel: Appropriately discards samples to conduct thinning and burn-in.
class SequentialMonteCarlo: Sequential Monte Carlo transition kernel.
class SequentialMonteCarloResults: Auxiliary results from a Sequential Monte Carlo step.
class Sharded: Shards a transition kernel across a named axis.
class StateWithHistory: StateWithHistory(state, state_history)
class ThinningKernel: Discards samples to perform thinning.
class TracingReducer: Reducer that accumulates trace results at each sample.
class VarianceReducer: Reducer that computes running variance.
class WeightedParticles: Particles with corresponding log weights.
class WithReductions: Applies Reducers to stream over MCMC samples.
class WithReductionsKernelResults: Reducer state and diagnostics for WithReductions.
Functions
augment_prior_with_state_history(...): Augments a prior or proposal distribution's state space with history.
augment_with_observation_history(...): Decorates a function to take observation_history.
augment_with_state_history(...): Decorates a transition or proposal fn to track state history.
chees_criterion(...): The ChEES criterion from [1].
chees_rate_criterion(...): ChEES rate criterion.
default_make_hmc_kernel_fn(...): Generate a hmc without transformation kernel.
ess_below_threshold(...): Determines if the effective sample size is much less than num_particles.
gen_make_hmc_kernel_fn(...): Generate a transformed hmc kernel.
gen_make_transform_hmc_kernel_fn(...): Generate a transformed hmc kernel.
infer_trajectories(...): Use particle filtering to sample from the posterior over trajectories.
init_near_unconstrained_zero(...): Returns an initialization Distribution for starting a Markov chain.
make_rwmh_kernel_fn(...): Generate a Random Walk MH kernel.
make_tqdm_progress_bar_fn(...): Make a progress_bar_fn that uses tqdm.
particle_filter(...): Samples a series of particles representing filtered latent states.
reconstruct_trajectories(...): Reconstructs the ancestor trajectory that generated each final particle.
remc_thermodynamic_integrals(...): Estimate thermodynamic integrals using results of ReplicaExchangeMC.
resample_deterministic_minimum_error(...): Deterministic minimum error resampler for sequential Monte Carlo.
resample_independent(...): Categorical resampler for sequential Monte Carlo.
resample_stratified(...): Stratified resampler for sequential Monte Carlo.
resample_systematic(...): A systematic resampler for sequential Monte Carlo.
retry_init(...): Tries an MCMC initialization proposal until it gets a valid state.
sample_chain(...): Runs a Markov chain defined by the given TransitionKernel.
sample_chain_with_burnin(...): Implements Markov chain Monte Carlo via repeated TransitionKernel steps.
sample_fold(...): Computes the requested reductions over the kernel's samples.
sample_sequential_monte_carlo(...): Runs Sequential Monte Carlo to sample from the posterior distribution.
simple_heuristic_tuning(...): Tune the number of steps and scaling of one mutation.
step_kernel(...): Takes num_steps repeated TransitionKernel steps from current_state.
windowed_adaptive_hmc(...): Adapt and sample from a joint distribution, conditioned on pins.
windowed_adaptive_nuts(...): Adapt and sample from a joint distribution using NUTS, conditioned on pins.