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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 ProgressBarReducer
: Reducer
that displays a progress bar.
class Reducer
: Base class for all MCMC Reducer
s.
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 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 Reducer
s 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].
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.
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.
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.
run_kernel(...)
: 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
.