CRF (version 0.4-3)

infer.conditional: Conditional inference method

Description

Computing the partition function and marginal probabilities

Usage

infer.conditional(crf, clamped, infer.method, ...)

Arguments

crf

The CRF

clamped

The vector of fixed values for clamped nodes, 0 for unfixed nodes

infer.method

The inference method to solve the clamped CRF

...

The parameters for infer.method

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.

Details

Conditional inference (takes another inference method as input)

Examples

# NOT RUN {
library(CRF)
data(Small)
i <- infer.conditional(Small$crf, c(0,1,0,0), infer.exact)

# }