| Safe Haskell | Safe-Infered |
|---|
Bayes.VariableElimination
Description
Algorithms for variable elimination
- priorMarginal :: (Graph g, Factor f, Show f) => BayesianNetwork g f -> EliminationOrder -> EliminationOrder -> f
- posteriorMarginal :: (Graph g, Factor f, Show f) => BayesianNetwork g f -> EliminationOrder -> EliminationOrder -> [DVI Int] -> f
- interactionGraph :: (FoldableWithVertex g, Factor f, UndirectedGraph g') => BayesianNetwork g f -> g' () DV
- degreeOrder :: (FoldableWithVertex g, Factor f, Graph g) => BayesianNetwork g f -> EliminationOrder -> Int
- minDegreeOrder :: (Graph g, Factor f, FoldableWithVertex g) => BayesianNetwork g f -> EliminationOrder
- minFillOrder :: (Graph g, Factor f, FoldableWithVertex g) => BayesianNetwork g f -> EliminationOrder
- allVariables :: (Graph g, Factor f) => BayesianNetwork g f -> [DV]
- marginal :: Factor f => [f] -> EliminationOrder -> EliminationOrder -> [DVI Int] -> f
- type EliminationOrder = [DV]
Inferences
Arguments
| :: (Graph g, Factor f, Show f) | |
| => BayesianNetwork g f | Bayesian Network |
| -> EliminationOrder | Ordering of variables to marginalize |
| -> EliminationOrder | Ordering of remaining to keep in result |
| -> f |
Compute the prior marginal. All the variables in the elimination order are conditionning variables ( p( . | conditionning variables) )
Arguments
| :: (Graph g, Factor f, Show f) | |
| => BayesianNetwork g f | Bayesian Network |
| -> EliminationOrder | Ordering of variables to marginzalie |
| -> EliminationOrder | Ordering of remaining variables |
| -> [DVI Int] | Assignment for some factors in vaiables to marginalize |
| -> f |
Interaction graph and elimination order
interactionGraph :: (FoldableWithVertex g, Factor f, UndirectedGraph g') => BayesianNetwork g f -> g' () DVSource
Compute the interaction graph of the BayesianNetwork
degreeOrder :: (FoldableWithVertex g, Factor f, Graph g) => BayesianNetwork g f -> EliminationOrder -> IntSource
Compute the degree order of an elimination order
minDegreeOrder :: (Graph g, Factor f, FoldableWithVertex g) => BayesianNetwork g f -> EliminationOrderSource
Elimination order minimizing the degree
minFillOrder :: (Graph g, Factor f, FoldableWithVertex g) => BayesianNetwork g f -> EliminationOrderSource
Elimination order minimizing the filling
allVariables :: (Graph g, Factor f) => BayesianNetwork g f -> [DV]Source
Get all variables from a Bayesian Network
Arguments
| :: Factor f | |
| => [f] | Bayesian Network |
| -> EliminationOrder | Ordering of variables to marginzalie |
| -> EliminationOrder | Ordering of remaining variables |
| -> [DVI Int] | Assignment for some factors in vaiables to marginalize |
| -> f |
Compute the prior marginal. All the variables in the elimination order are conditionning variables ( p( . | conditionning variables) )
type EliminationOrder = [DV]Source
Elimination order