infer.cutset: Inference method for graphs with a small cutset

In CRF: Conditional Random Fields

Description

Computing the partition function and marginal probabilities

Usage

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

Arguments

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".

Details

Exact inference for graphs with a small cutset using cutset conditioning

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.cutset(Small$crf, c(2))

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