monad-bayes-1.0.0: benchmark/SSM.hs

module Main where

import Control.Monad.Bayes.Inference.MCMC
import Control.Monad.Bayes.Inference.PMMH as PMMH (pmmh)
import Control.Monad.Bayes.Inference.RMSMC (rmsmcDynamic)
import Control.Monad.Bayes.Inference.SMC
import Control.Monad.Bayes.Inference.SMC2 as SMC2 (smc2)
import Control.Monad.Bayes.Population
import Control.Monad.Bayes.Population (population, resampleMultinomial)
import Control.Monad.Bayes.Sampler.Strict (sampleIO, sampleIOfixed, sampleWith)
import Control.Monad.Bayes.Weighted (unweighted)
import Control.Monad.IO.Class (MonadIO (liftIO))
import NonlinearSSM (generateData, model, param)
import System.Random.Stateful (mkStdGen, newIOGenM)

main :: IO ()
main = sampleIOfixed $ do
  let t = 5
  dat <- generateData t
  let ys = map snd dat
  liftIO $ print "SMC"
  smcRes <- population $ smc SMCConfig {numSteps = t, numParticles = 10, resampler = resampleMultinomial} (param >>= model ys)
  liftIO $ print $ show smcRes
  liftIO $ print "RM-SMC"
  smcrmRes <-
    population $
      rmsmcDynamic
        MCMCConfig {numMCMCSteps = 10, numBurnIn = 0, proposal = SingleSiteMH}
        SMCConfig {numSteps = t, numParticles = 10, resampler = resampleSystematic}
        (param >>= model ys)
  liftIO $ print $ show smcrmRes
  liftIO $ print "PMMH"
  pmmhRes <-
    unweighted $
      pmmh
        MCMCConfig {numMCMCSteps = 2, numBurnIn = 0, proposal = SingleSiteMH}
        SMCConfig {numSteps = t, numParticles = 3, resampler = resampleSystematic}
        param
        (model ys)
  liftIO $ print $ show pmmhRes
  liftIO $ print "SMC2"
  smc2Res <- population $ smc2 t 3 2 1 param (model ys)
  liftIO $ print $ show smc2Res