bayes-stack-0.2.0.1: Framework for inferring generative probabilistic models with Gibbs sampling

Safe HaskellNone

BayesStack.Dirichlet

Contents

Synopsis

Dirichlet parameter

data Alpha a Source

A Dirichlet prior

Instances

Eq a => Eq (Alpha a) 
(Enum a, Show a) => Show (Alpha a) 
Generic (Alpha a) 
(Enum a, Serialize a) => Serialize (Alpha a) 

symAlpha :: Enum a => [a] -> Double -> Alpha aSource

asymAlpha :: Enum a => EnumMap a Double -> Alpha aSource

Construct an asymmetric Alpha

alphaDomain :: Enum a => Alpha a -> Seq aSource

'alphaDomain a' is the domain of prior a

alphaNormalizer :: Enum a => Alpha a -> LogFloatSource

sumAlpha :: Enum a => Alpha a -> DoubleSource

'sumAlpha alpha' is the sum of all alphas

type DirMean a = EnumMap a DoubleSource

type DirPrecision = DoubleSource

alphaOf :: Enum a => Alpha a -> a -> DoubleSource

'alphaOf alpha k' is the value of element k in prior alpha

setAlphaOf :: Enum a => a -> Double -> Alpha a -> Alpha aSource

Set a particular alpha element

setSymAlpha :: Enum a => Double -> Alpha a -> Alpha aSource

alphaToMeanPrecision :: Enum a => Alpha a -> (DirMean a, DirPrecision)Source

'alphaToMeanPrecision a' is the mean/precision representation of the prior a

meanPrecisionToAlpha :: Enum a => DirMean a -> DirPrecision -> Alpha aSource

'meanPrecisionToAlpha m p' is a prior with mean m and precision p

symmetrizeAlpha :: Enum a => Alpha a -> Alpha aSource

Symmetrize a Dirichlet prior (such that mean=0)

prettyAlpha :: Enum a => (a -> String) -> Alpha a -> DocSource

Pretty-print a Dirichlet prior