Bayes.Continuous

Types

For the graph description

type CNMonad a = GraphMonad DirectedSG () Distri a

The Bayesian monad

data CV

A continuous variable

Instances

Eq CV

Ord CV

Show CV

BayesianVariable CV

Instantiable CV Double CVI

SampleGeneration CV CVI (Double, Double) Double

data DN

An expression which can be a constant, variable or formula. In case it is a variable, it can be used as a BayesianVariable or instantiated as an Instantiable type. Otherwise you'll get an error

Instances

Fractional DN

Num DN

BayesianVariable DN

Instantiable DN Double CVI

class VariableName m where

This class is used to simplify the network description. Variable names can be optional. In that later case, () must be used instead of a name.

Methods

mkVariable :: m -> CNMonad CV

Instances

VariableName String

VariableName ()

class BayesianVariable v where

A Bayesian Variable is a variable part of Bayesian network and so which knows its position : the vertex.

Methods

vertex :: v -> Vertex

Instances

BayesianVariable DVI

BayesianVariable DV

BayesianVariable CVI

BayesianVariable CV

BayesianVariable Vertex

BayesianVariable PorD

BayesianVariable DEV

BayesianVariable DN

BayesianVariable (TDV s)

For the sampling

data Distri

type ContinuousNetwork = SBN Distri

type ContinuousSample = SBN CVI

class InstantiationValue i v | i -> v where

Methods

instantiationValue :: i -> v

toDouble :: i -> Double

Instances

InstantiationValue DVI Int

InstantiationValue CVI Double

data CVI

A continuous variable instantiation

Instances

Eq CVI

Ord CVI

Show CVI

BayesianVariable CVI

InstantiationValue CVI Double

Instantiable CV Double CVI

Instantiable DN Double CVI

SampleGeneration CV CVI (Double, Double) Double

Graph g => Show (Sample g CVI)

Network creation functions

uniform

Arguments

:: VariableName s

=> s

Variable name

-> DN

Min bound

-> DN

Max bound

-> CNMonad DN

Uniform dstribution

normal

Arguments

:: VariableName s

=> s

Variable name

-> DN

Average

-> DN

Standard deviation

-> CNMonad DN

Normal distribution

beta :: VariableName s => s -> DN -> DN -> CNMonad DN

Beta distribution

beta' :: VariableName s => s -> DN -> DN -> CNMonad DN

Beta' distribution

exponential :: VariableName s => s -> DN -> CNMonad DN

Exponential distribution

execCN :: CNMonad a -> ContinuousNetwork

Create a network but only returns the monad value. Mainly used for testing.

runCN :: CNMonad a -> (a, ContinuousNetwork)

Create a network using the simple graph implementation The initialized nodes are replaced by the value. Returns the monad values and the built graph.

evalCN :: CNMonad a -> a

Create a bayesian network but only returns the monad value. Mainly used for testing.

Sampling functions

runSampling

Arguments

:: (DirectedGraph g, FunctorWithVertex g)

=> Int

Number of used samples

-> Int

Burnin samples before the used samples

-> Sampler g a

Sampler

-> IO [Sample g a]

List of generated samples

Sample a bayesian network using a given sampling scheme

continuousMCMCSampler

Arguments

:: ContinuousNetwork

Bayesian network

-> [CVI]

Evidence

-> Sampler DirectedSG CVI

Sampler

Gibbs sampling for continuous network

(=:) :: Instantiable d v r => d -> v -> r

Create a variable instantiation using values from an enumeration

Result statistics

histogram

Arguments

:: Int

Bins

-> [Double]

Samples

-> [(Double, Double, Double)]

Histogram (minBound,maxBound, value)

Compute the histogram of values

samplingHistograms

Arguments

:: (InstantiationValue i v, BayesianVariable i, FunctorWithVertex g, Graph g)

=> Int

Number of bins

-> [Sample g i]

Samples

-> Sample g [(Double, Double, Double)]

Histogram with start bins and bin content

Generate a graph of sampling histogram for each variable So, for a vertex v we have the posterior values p(v)