mrfse_sa_con: Cnservative approach for Frondana's mrfse using simulated...

In mrfse: Markov Random Field Structure Estimator

Cnservative approach for Frondana's mrfse using simulated annealing

Description

A penalized likelihood BIC-based to estimate Markovian neighborhoods.

Usage

mrfse.sa.con(a_size, sample, c, t0, iterations=1000, max_neigh=ncol(sample)-1)

Arguments

a_size	Size of the alphabet.
sample	A integer-valued matrix. Each value must belong range 0 and a_size - 1. Matrix has dimension n x V, where n is number of samples and V is number of nodes.
c	The penalization constant. Must be positive.
t0	Inital temperature
iterations	Number of simulated annealing iterations
max_neigh	The maximum length of a candidate Markovian neighborhood. Must be non-negative and less than ncol(sample).

Value

A adjacency matrix of the estimated Markov random field graph.

Author(s)

Rodrigo Carvalho

References

FRONDANA, Iara Moreira. Model selection for discrete Markov random fields on graphs. São Paulo : Instituto de Matemática e Estatística, University of São Paulo, 2016. Doctoral Thesis in Estatística. <doi:10.11606/T.45.2018.tde-02022018-151123> http://www.teses.usp.br/teses/disponiveis/45/45133/tde-02022018-151123/publico/tese_Iara_Frondana.pdf

Examples

library(mrfse)
a_size = c(0, 1)
s = matrix(sample(a_size, size=1000, replace=TRUE), ncol=5)
mrfse.sa.con(length(a_size), s, 1.0, 500, 1000)

mrfse documentation built on Nov. 16, 2022, 5:06 p.m.