Module: tfp.experimental.mcmc

TensorFlow Probability experimental NUTS package.

Classes

class EllipticalSliceSampler: Runs one step of the elliptic slice sampler.

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

class SequentialMonteCarlo: Sequential Monte Carlo transition kernel.

class SequentialMonteCarloResults: Auxiliary results from a Sequential Monte Carlo step.

class StateWithHistory: StateWithHistory(state, state_history)

class WeightedParticles: Particles with corresponding log weights.

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.

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.

infer_trajectories(...): Use particle filtering to sample from the posterior over trajectories.

make_rwmh_kernel_fn(...): Generate a Random Walk MH kernel.

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.

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.