CRF (version 0.3-6)

infer.trbp: Inference method using tree-reweighted belief propagation

Description

Computing the partition function and marginal probabilities

Usage

infer.trbp(crf, max.iter = 10000, cutoff = 1e-04, verbose = 0)

Arguments

crf

The CRF

max.iter

The maximum allowed iterations of termination criteria

cutoff

The convergence cutoff of termination criteria

verbose

Non-negative integer to control the tracing informtion in algorithm

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

Approximate inference using sum-product tree-reweighted belief propagation

Examples

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