monad-bayes-0.1.0.0: src/Control/Monad/Bayes/Inference/PMMH.hs

{-|
Module      : Control.Monad.Bayes.Inference.PMMH
Description : Particle Marginal Metropolis-Hastings (PMMH)
Copyright   : (c) Adam Scibior, 2015-2020
License     : MIT
Maintainer  : leonhard.markert@tweag.io
Stability   : experimental
Portability : GHC

Particle Marginal Metropolis-Hastings (PMMH) sampling.

Christophe Andrieu, Arnaud Doucet, and Roman Holenstein. 2010. Particle Markov chain Monte Carlo Methods. /Journal of the Royal Statistical Society/ 72 (2010), 269-342. <http://www.stats.ox.ac.uk/~doucet/andrieu_doucet_holenstein_PMCMC.pdf>
-}

module Control.Monad.Bayes.Inference.PMMH (
  pmmh
)  where

import Numeric.Log

import Control.Monad.Trans (lift)

import Control.Monad.Bayes.Class
import Control.Monad.Bayes.Sequential
import Control.Monad.Bayes.Population as Pop
import Control.Monad.Bayes.Traced
import Control.Monad.Bayes.Inference.SMC

-- | Particle Marginal Metropolis-Hastings sampling.
pmmh :: MonadInfer m
     => Int -- ^ number of Metropolis-Hastings steps
     -> Int -- ^ number of time steps
     -> Int -- ^ number of particles
     -> Traced m b -- ^ model parameters prior
     -> (b -> Sequential (Population m) a) -- ^ model
     -> m [[(a, Log Double)]]
pmmh t k n param model =
  mh t (param >>= runPopulation . pushEvidence . Pop.hoist lift . smcSystematic k n . model)