R: Inference method using tree-reweighted belief propagation

infer.trbp {CRF} | R Documentation

## 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,
      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)

[Package CRF version 0.4-3 Index]