R: Inference method using sampling

infer.sample {CRF} | R Documentation

## Inference method using sampling

### Description

Computing the partition function and marginal probabilities

### Usage

    infer.sample(crf, sample.method, ...)

### Arguments

`crf` | The CRF
`sample.method` | The sampling method
`...` | The parameters for `sample.method`

### Details

Approximate inference using sampling (takes a sampling method as input)

### 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.sample(Small$crf, sample.exact, 10000)

[Package CRF version 0.4-3 Index]