| Copyright | (c) Adam Scibior 2015-2020 |
|---|---|
| License | MIT |
| Maintainer | leonhard.markert@tweag.io |
| Stability | experimental |
| Portability | GHC |
| Safe Haskell | Safe-Inferred |
| Language | Haskell2010 |
Control.Monad.Bayes.Inference.PMMH
Description
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
Synopsis
- pmmh :: MonadDistribution m => MCMCConfig -> SMCConfig (WeightedT m) -> TracedT (WeightedT m) a1 -> (a1 -> SequentialT (PopulationT (WeightedT m)) a2) -> m [[(a2, Log Double)]]
- pmmhBayesianModel :: MonadMeasure m => MCMCConfig -> SMCConfig (WeightedT m) -> (forall m'. MonadMeasure m' => Bayesian m' a1 a2) -> m [[(a2, Log Double)]]
Documentation
pmmh :: MonadDistribution m => MCMCConfig -> SMCConfig (WeightedT m) -> TracedT (WeightedT m) a1 -> (a1 -> SequentialT (PopulationT (WeightedT m)) a2) -> m [[(a2, Log Double)]] Source #
Particle Marginal Metropolis-Hastings sampling.
pmmhBayesianModel :: MonadMeasure m => MCMCConfig -> SMCConfig (WeightedT m) -> (forall m'. MonadMeasure m' => Bayesian m' a1 a2) -> m [[(a2, Log Double)]] Source #
Particle Marginal Metropolis-Hastings sampling from a Bayesian model