one.step: Metropolis-Hastings Simulation

In simecek/mhnet: Metropolis-Hastings type simulation and inference for Bayesian Networks

Description

Do one step of Metropolis-Hastings algorithm: Propose a new graph and either accept or decline it.

Usage

one.step(nw, dataset, verbose = TRUE, score = BICbnnet, whitelist = NULL,
  blacklist = NULL, lprior = log.prior, move.score = NULL,
  move.lprior = NULL, move.proposals = NULL)

Arguments

nw	mhnet network
dataset	A data frame to use
verbose	If TRUE then report messanges and plots will be given
score	A function to calculate BIC
whitelist	For move proposal, 2-column matrix or data.frame with compulsory edges (vertices as numbers)
blacklist	For move proposal, 2-column matrix or data.frame with forbidden edges (vertices as numbers)
lprior	Log of prior distribution of acyclic directed graph
move.score	A function to adjust the score to addition / removal / reversal of an edge
move.lprior	A function to adjust the log-prior to addition / removal / reversal of an edge
move.proposals	A function to adjust the proposals to addition / removal / reversal of an edge

Value

mhnet network

Examples

rg <- tabu(learning.test)
nw <- import.bnlearn(rg)
one.step(nw, learning.test)

simecek/mhnet documentation built on May 29, 2019, 10:01 p.m.