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.Weighted

Description

WeightedT is an instance of MonadFactor. Apply a MonadDistribution transformer to obtain a MonadMeasure that can execute probabilistic models.

Synopsis

data WeightedT m a
weightedT :: Monad m => m (a, Log Double) -> WeightedT m a
extractWeight :: Functor m => WeightedT m a -> m (Log Double)
unweighted :: Functor m => WeightedT m a -> m a
applyWeight :: MonadFactor m => WeightedT m a -> m a
hoist :: (forall x. m x -> n x) -> WeightedT m a -> WeightedT n a
runWeightedT :: WeightedT m a -> m (a, Log Double)

Documentation

data WeightedT m a Source #

Execute the program using the prior distribution, while accumulating likelihood.

Instances

Instances details

MonadTrans WeightedT Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods lift :: Monad m => m a -> WeightedT m a #
MonadIO m => MonadIO (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods liftIO :: IO a -> WeightedT m a #
Monad m => Applicative (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods pure :: a -> WeightedT m a # (<>) :: WeightedT m (a -> b) -> WeightedT m a -> WeightedT m b # liftA2 :: (a -> b -> c) -> WeightedT m a -> WeightedT m b -> WeightedT m c # (>) :: WeightedT m a -> WeightedT m b -> WeightedT m b # (<*) :: WeightedT m a -> WeightedT m b -> WeightedT m a #
Functor m => Functor (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods fmap :: (a -> b) -> WeightedT m a -> WeightedT m b # (<$) :: a -> WeightedT m b -> WeightedT m a #
Monad m => Monad (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods (>>=) :: WeightedT m a -> (a -> WeightedT m b) -> WeightedT m b # (>>) :: WeightedT m a -> WeightedT m b -> WeightedT m b # return :: a -> WeightedT m a #
MonadDistribution m => MonadDistribution (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods random :: WeightedT m Double Source # uniform :: Double -> Double -> WeightedT m Double Source # normal :: Double -> Double -> WeightedT m Double Source # gamma :: Double -> Double -> WeightedT m Double Source # beta :: Double -> Double -> WeightedT m Double Source # bernoulli :: Double -> WeightedT m Bool Source # categorical :: Vector v Double => v Double -> WeightedT m Int Source # logCategorical :: (Vector v (Log Double), Vector v Double) => v (Log Double) -> WeightedT m Int Source # uniformD :: [a] -> WeightedT m a Source # geometric :: Double -> WeightedT m Int Source # poisson :: Double -> WeightedT m Int Source # dirichlet :: Vector v Double => v Double -> WeightedT m (v Double) Source #
Monad m => MonadFactor (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted Methods score :: Log Double -> WeightedT m () Source #
MonadDistribution m => MonadMeasure (WeightedT m) Source #
Instance details Defined in Control.Monad.Bayes.Weighted

weightedT :: Monad m => m (a, Log Double) -> WeightedT m a Source #

Embed a random variable with explicitly given likelihood.

runWeightedT . weightedT = id

extractWeight :: Functor m => WeightedT m a -> m (Log Double) Source #

Compute the weight and discard the sample.

unweighted :: Functor m => WeightedT m a -> m a Source #

Compute the sample and discard the weight.

This operation introduces bias.

applyWeight :: MonadFactor m => WeightedT m a -> m a Source #

Use the weight as a factor in the transformed monad.

hoist :: (forall x. m x -> n x) -> WeightedT m a -> WeightedT n a Source #

Apply a transformation to the transformed monad.

runWeightedT :: WeightedT m a -> m (a, Log Double) Source #

Obtain an explicit value of the likelihood for a given value.