Language.Passage

Types

data BayesianNetwork a

Instances

Monad BayesianNetwork

Functor BayesianNetwork

data BayesianGraph

A Bayesian Network is a collection of stochastic nodes. Stochastic nodes may be (optionally) grouped into arrays.

Constructors

BayesianGraph

Fields

stoNodes :: !(IntMap StoVar)

stoArryas :: !(IntMap ArrayInfo)

Instances

Show BayesianGraph

PP BayesianGraph

LaTeX BayesianGraph

data StoVar

A Stochastic variable.

Constructors

StoVar

Fields

stoVarName :: StoVarName

stoVarPrior :: PriorInfo

stoPostDistLL :: !(Map Expr Expr)

Maps terms that mention the variable to their coefficients, which do not depend on the variable. The term for the distribution is the sum of the products of the map elements (see stoPostLL).

Instances

Show StoVar

type Node = Expr

data BayesianSimulator a

Instances

Monad BayesianSimulator

Functor BayesianSimulator

Constructing models

logGamma :: Term a -> Term a

using :: Distribution -> BayesianNetwork Expr

tconst :: Double -> Term a

vector

Arguments

:: (Int, Int)

Bounds for the vector indexes

-> (Int -> BayesianNetwork Expr)

Initializer (should return a node)

-> BayesianNetwork ([Expr] -> Expr)

Create a 1D vector

matrix

Arguments

:: (Int, Int)

-> (Int, Int)

Bounds for 1st and 2nd dimensions.

-> ([Int] -> BayesianNetwork Expr)

Initializer

-> BayesianNetwork ([Expr] -> Expr)

Create a 2D matrix

nodeArray

Arguments

:: [(Int, Int)]

Bounds for each dimension.

-> ([Int] -> BayesianNetwork Expr)

Initializer

-> BayesianNetwork ([Expr] -> Expr)

Create an >= 3D array

type Vector = [Expr] -> Expr

type Matrix = [Expr] -> Expr

type NodeArray = [Expr] -> Expr

tcase :: Term a -> [Term a] -> Term a

(//) :: BayesianNetwork Expr -> String -> BayesianNetwork Expr

Distributions

Extracting graphs

buildBayesianGraph :: BayesianNetwork a -> (a, BayesianGraph)

Displayihng graphs

class Show t => PP t where

Pretty printing

Methods

ppPrec :: Rational -> t -> Doc

pp :: t -> Doc

Instances

PP Char

PP Double

PP Int

PP Integer

PP Word

PP Op

PP BayesianGraph

PP PPVar

PP a => PP (Term a)

class LaTeX t where

Methods

latex :: t -> Doc

Instances

LaTeX Double

LaTeX Int

LaTeX BayesianGraph

LaTeX PPVar

LaTeX a => LaTeX (Term a)

Simulation

simulate :: FilePath -> BayesianSimulator () -> IO ()

genSimulator :: FilePath -> BayesianSimulator () -> IO ()

setSampleCount :: Int -> BayesianSimulator ()

setIterationsPerSample :: Int -> BayesianSimulator ()

setWarmupCount :: Int -> BayesianSimulator ()

setThreadNum :: Int -> BayesianSimulator ()

useMersenneTwister :: Bool -> BayesianSimulator ()

enableProfiling :: Bool -> BayesianSimulator ()

setRandomSeed :: Int -> BayesianSimulator ()

Set the random seed for a thread. This function may be calledd multiple times to set the seeds for multiple threads. The seeds are used in order: first call is for thread 0, next for thread 1, etc.

useSpecialSlicers :: Bool -> BayesianSimulator ()

When using a specialized slizer, we generate a custom slicer for each stochastic variable. The benefit of this is that, in principle, this may result in more efficient code, at the cost of longer compilation time, and larger binary. The alternative is to use a generic slicing function which is parameterized by the log-likelihood function for a variable.

splitFiles :: Bool -> BayesianSimulator ()

Generate a separate file for each stochastic variable. The benefit of this flag is that it makes it possible to compile multiple files in parallel. The drawback is that some optimizations may be lost because the files are compiled separately. Also, there is some overhead for processing multiple files.

model :: BayesianNetwork a -> BayesianSimulator a

observe :: Term NodeIdx -> Double -> BayesianSimulator ()

monitor :: String -> Expr -> BayesianSimulator ()

monitorVec :: String -> Matrix -> [Int] -> BayesianSimulator ()

Like monitor, but adds the indexes in the label of the variable.

monitorVecs :: String -> NodeArray -> [[Int]] -> BayesianSimulator ()

LaTeX

runLatex :: BayesianNetwork a -> IO ()