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

In CRF: Conditional Random Fields

Description

Computing the partition function and marginal probabilities

Usage

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

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
maximize	Logical variable to indicate using max-product instead of sum-product

Details

Approximate inference using sum-product tree-reweighted belief propagation

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.trbp(Small$crf)

CRF documentation built on Dec. 1, 2019, 3:02 a.m.