hbayes-0.1: Inference with Discrete Bayesian Networks

Safe HaskellSafe-Infered




Algorithms for factor elimination


Moral graph

moralGraph :: (NamedGraph g, FoldableWithVertex g, DirectedGraph g) => g () b -> UndirectedSG () bSource

For the junction tree construction, only the vertices are needed during the intermediate steps. So, the moral graph is returned without any vertex data.


nodeComparisonForTriangulation :: (UndirectedGraph g, Factor f) => g a f -> Vertex -> Vertex -> OrderingSource

Node selection comparison function used for triangulating the graph

numberOfAddedEdges :: UndirectedGraph g => g a b -> Vertex -> IntSource

Number of edges added when connecting all neighbors



:: Graph g 
=> (Vertex -> Vertex -> Ordering)

Criterion function for triangulation

-> g () b 
-> ([VertexCluster], g () b)

Returns the clusters and the triangulated graph

Triangulate a graph using a cost function The result is the triangulated graph and the list of clusters which may not be maximal.

Junction tree

minimumSpanningTree :: UndirectedGraph g => g Int f -> Tree () fSource

Implementing the Prim's algorithm for minimum spanning tree

data Cluster Source

Cluster of discrete variables. Discrete variables instead of vertices are needed because the factor are using DV and we need to find which factors must be contained in a given cluster.




:: (DirectedGraph g, FoldableWithVertex g, NamedGraph g, Factor f, Show f) 
=> (UndirectedSG () f -> Vertex -> Vertex -> Ordering)

Weight function on the moral graph

-> BayesianNetwork g f

Input directed graph

-> JunctionTree f

Junction tree

Create a function tree

type JunctionTree f = Tree (Separator f) (JTNodeValue f)Source

The junction tree

Shenoy-Shafer message passing

collect :: Factor f => JunctionTree f -> JunctionTree fSource

Collect phase of the junction tree

distribute :: Factor f => Maybe (Separator f) -> JunctionTree f -> JunctionTree fSource

Distribute phase of the junction tree

posterior :: Factor f => JunctionTree f -> DV -> Maybe fSource

Compute the marginal posterior (if some evidence is set on the junction tree) otherwise compute just the marginal prior.


clearEvidence :: Factor f => JunctionTree f -> JunctionTree fSource

Remove evidence in the junction tree

updateEvidence :: Factor f => DVISet Int -> JunctionTree f -> JunctionTree fSource

Update evidence in the tree




:: (DirectedGraph g, FoldableWithVertex g, NamedGraph g) 
=> (UndirectedSG () b -> Vertex -> Vertex -> Ordering)

Weight function on the moral graph

-> g () b

Input directed graph

-> Tree () VertexCluster

Junction tree

Create a junction tree with only the clusters and no factors

data VertexCluster Source

A cluster containing only the vertices and not yet the factors