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.Sampler.Strict

Description

SamplerIO and SamplerST are instances of MonadDistribution. Apply a MonadFactor transformer to obtain a MonadMeasure that can execute probabilistic models.

Synopsis

newtype SamplerT g m a = SamplerT {
- runSamplerT :: ReaderT g m a
}
type SamplerIO = SamplerT (IOGenM StdGen) IO
type SamplerST s = SamplerT (STGenM StdGen s) (ST s)
sampleIO :: SamplerIO a -> IO a
sampleIOfixed :: SamplerIO a -> IO a
sampleWith :: SamplerT g m a -> g -> m a
sampleSTfixed :: SamplerST s b -> ST s b
sampleMean :: [(Double, Log Double)] -> Double
sampler :: SamplerIO a -> IO a

Documentation

newtype SamplerT g m a Source #

The sampling interpretation of a probabilistic program Here m is typically IO or ST

Constructors

SamplerT
Fields runSamplerT :: ReaderT g m a

Instances

Instances details

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

type SamplerIO = SamplerT (IOGenM StdGen) IO Source #

convenient type synonym to show specializations of SamplerT to particular pairs of monad and RNG

type SamplerST s = SamplerT (STGenM StdGen s) (ST s) Source #

convenient type synonym to show specializations of SamplerT to particular pairs of monad and RNG

sampleIO :: SamplerIO a -> IO a Source #

initialize random seed using system entropy, and sample

sampleIOfixed :: SamplerIO a -> IO a Source #

Run the sampler with a fixed random seed

sampleWith :: SamplerT g m a -> g -> m a Source #

Sample with a random number generator of your choice e.g. the one from Random.

>>> import Control.Monad.Bayes.Class
>>> import System.Random.Stateful hiding (random)
>>> newIOGenM (mkStdGen 1729) >>= sampleWith random
4.690861245089605e-2

sampleSTfixed :: SamplerST s b -> ST s b Source #

Run the sampler with a fixed random seed

sampleMean :: [(Double, Log Double)] -> Double Source #

sampler :: SamplerIO a -> IO a Source #

initialize random seed using system entropy, and sample