infer.tree {CRF}

Inference method for tree- and forest-structured graphs

Description

Computing the partition function and marginal probabilities

Usage

infer.tree(crf)

Arguments

crf

The CRF

Details

Exact inference for tree- and forest-structured graphs with sum-product belief propagation

Value

This function will return a list with components:

node.bel

Node belief. It is a matrix with crf$n.nodes rows and crf$max.state columns.

edge.bel

Edge belief. It is a list of matrices. The size of list is crf$n.edges and the matrix i has crf$n.states[crf$edges[i,1]] rows and crf$n.states[crf$edges[i,2]] columns.

logZ

The logarithmic value of CRF normalization factor Z.

Examples

library(CRF)
data(Small)
i <- infer.tree(Small$crf)