infer.tree: Inference method for tree- and forest-structured graphs
In CRF: Conditional Random Fields

Computing the partition function and marginal probabilities

1	infer.tree(crf)

crf

The CRF

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

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.

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

CRF documentation built on Dec. 2, 2019, 1:11 a.m.

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