Control.Monad.Bayes.Inference.SMC

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.

Documentation

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

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

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

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

Constructors

SMCConfig

Fields

• resampler :: forall x. PopulationT m x -> PopulationT m x
• numSteps :: Int
• numParticles :: Int