R: Inference method for graphs with a small cutset

infer.cutset {CRF}

R Documentation

Inference method for graphs with a small cutset

Computing the partition function and marginal probabilities

infer.cutset(crf, cutset, engine = "default")

`crf`	The CRF
`cutset`	A vector of nodes in the cutset
`engine`	The underlying engine for cutset decoding, possible values are "default", "none", "exact", "chain", and "tree".

Exact inference for graphs with a small cutset using cutset conditioning

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.


library(CRF)
data(Small)
i <- infer.cutset(Small$crf, c(2))

[Package CRF version 0.4-3 Index]