Copyright	(c) Adam Scibior 2015-2020
License	MIT
Maintainer	leonhard.markert@tweag.io
Stability	experimental
Portability	GHC
Safe Haskell	None
Language	Haskell2010

Control.Monad.Bayes.Sampler

Description

SamplerIO and SamplerST are instances of MonadSample. Apply a MonadCond transformer to obtain a MonadInfer that can execute probabilistic models.

Synopsis

data SamplerIO a
sampleIO :: SamplerIO a -> IO a
sampleIOfixed :: SamplerIO a -> IO a
sampleIOwith :: SamplerIO a -> GenIO -> IO a
data Seed
newtype SamplerST a = SamplerST (forall s. ReaderT (GenST s) (ST s) a)
runSamplerST :: SamplerST a -> ReaderT (GenST s) (ST s) a
sampleST :: SamplerST a -> State Seed a
sampleSTfixed :: SamplerST a -> a

Documentation

data SamplerIO a Source #

An IO based random sampler using the MWC-Random package.

Instances

Instances details

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

sampleIO :: SamplerIO a -> IO a Source #

Initialize a pseudo-random number generator using randomness supplied by the operating system. For efficiency this operation should be applied at the very end, ideally once per program.

sampleIOfixed :: SamplerIO a -> IO a Source #

Like sampleIO, but with a fixed random seed. Useful for reproducibility.

sampleIOwith :: SamplerIO a -> GenIO -> IO a Source #

Like sampleIO but with a custom pseudo-random number generator.

data Seed #

An immutable snapshot of the state of a Gen.

Instances

Instances details

Eq Seed
Instance details Defined in System.Random.MWC Methods (==) :: Seed -> Seed -> Bool # (/=) :: Seed -> Seed -> Bool #
Show Seed
Instance details Defined in System.Random.MWC Methods showsPrec :: Int -> Seed -> ShowS # show :: Seed -> String # showList :: [Seed] -> ShowS #

newtype SamplerST a Source #

An ST based random sampler using the mwc-random package.

Constructors

SamplerST (forall s. ReaderT (GenST s) (ST s) a)

Instances

Instances details

Monad SamplerST Source #
Instance details Defined in Control.Monad.Bayes.Sampler Methods (>>=) :: SamplerST a -> (a -> SamplerST b) -> SamplerST b # (>>) :: SamplerST a -> SamplerST b -> SamplerST b # return :: a -> SamplerST a #
Functor SamplerST Source #
Instance details Defined in Control.Monad.Bayes.Sampler Methods fmap :: (a -> b) -> SamplerST a -> SamplerST b # (<$) :: a -> SamplerST b -> SamplerST a #
Applicative SamplerST Source #
Instance details Defined in Control.Monad.Bayes.Sampler Methods pure :: a -> SamplerST a # (<>) :: SamplerST (a -> b) -> SamplerST a -> SamplerST b # liftA2 :: (a -> b -> c) -> SamplerST a -> SamplerST b -> SamplerST c # (>) :: SamplerST a -> SamplerST b -> SamplerST b # (<*) :: SamplerST a -> SamplerST b -> SamplerST a #
MonadSample SamplerST Source #
Instance details Defined in Control.Monad.Bayes.Sampler Methods random :: SamplerST Double Source # uniform :: Double -> Double -> SamplerST Double Source # normal :: Double -> Double -> SamplerST Double Source # gamma :: Double -> Double -> SamplerST Double Source # beta :: Double -> Double -> SamplerST Double Source # bernoulli :: Double -> SamplerST Bool Source # categorical :: Vector v Double => v Double -> SamplerST Int Source # logCategorical :: (Vector v (Log Double), Vector v Double) => v (Log Double) -> SamplerST Int Source # uniformD :: [a] -> SamplerST a Source # geometric :: Double -> SamplerST Int Source # poisson :: Double -> SamplerST Int Source # dirichlet :: Vector v Double => v Double -> SamplerST (v Double) Source #

runSamplerST :: SamplerST a -> ReaderT (GenST s) (ST s) a Source #

sampleST :: SamplerST a -> State Seed a Source #

Run the sampler with a supplied seed. Note that 'State Seed' is much less efficient than SamplerST for composing computation.

sampleSTfixed :: SamplerST a -> a Source #

Run the sampler with a fixed random seed.