R: Inference method using residual belief propagation

infer.rbp {CRF} | R Documentation

## Inference method using residual belief propagation

### Description

Computing the partition function and marginal probabilities

### Usage

    infer.rbp(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 residual 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.rbp(Small$crf)

[Package CRF version 0.4-3 Index]