monad-bayes-1.3.0.1: A library for probabilistic programming.
Copyright(c) Adam Scibior 2015-2020
LicenseMIT
Maintainerleonhard.markert@tweag.io
Stabilityexperimental
PortabilityGHC
Safe HaskellSafe-Inferred
LanguageHaskell2010

Control.Monad.Bayes.Inference.SMC

Description

Sequential Monte Carlo (SMC) sampling.

Arnaud Doucet and Adam M. Johansen. 2011. A tutorial on particle filtering and smoothing: fifteen years later. In The Oxford Handbook of Nonlinear Filtering, Dan Crisan and Boris Rozovskii (Eds.). Oxford University Press, Chapter 8.

Synopsis

Documentation

smc :: MonadDistribution m => SMCConfig m -> SequentialT (PopulationT m) a -> PopulationT m a Source #

Sequential importance resampling. Basically an SMC template that takes a custom resampler.

smcPush :: MonadMeasure m => SMCConfig m -> SequentialT (PopulationT m) a -> PopulationT m a Source #

Sequential Monte Carlo with multinomial resampling at each timestep. Weights are normalized at each timestep and the total weight is pushed as a score into the transformed monad.

data SMCConfig m Source #

Constructors

SMCConfig 

Fields