exactposterior: Posterior distribution on Bayesian networks.

In rjbgoudie/structmcmc: Structural inference for Bayesian Networks and Variable Selection.

Description

Use one of a number of methods to get the posterior distribution

Usage

  exactposterior(data,
    prior = priorUniform(empty(ncol(data), "bn")),
    logScoreFUN = logScoreMultDirFUN(),
    logScoreParameters = list(hyperparameters = "bdeu"),
    constraint = NULL, maxNumberParents = NULL,
    verbose = T)

Arguments

data	The data.
prior	A list of functions of the same length as initial that returns the local prior score of the corresponding node, given a numeric vector of parents. The default value NULL uses an improper uniform prior.
logScoreFUN	A list of four elements: offline A function that computes the logScore of a Bayesian Network online A function that incrementally computes the logScore of a Bayesian Network local A function that computes the local logScore of a Bayesian Network prepare A function that prepares the data, and any further pre-computation required by the logScore functions.
logScoreParameters	A list of parameters that are passed to logScoreFUN.
constraint	A matrix of dimension ncol(data) x ncol(data) giving constraints to the sample space. The (i, j) element is 1 if the edge i -> j is required -1 if the edge i -> is excluded. 0 if the edge i -> j is not constrained. The diagonal of constraint must be all 0.
maxNumberParents	Integer of length 1. The maximum number of parents of any node. A NULL value uses the ncol(data) - 1.
verbose	A logical. Should a progress bar be displayed?

Value

A bnpost object.

See Also

posterior. Example priors priorGraph, priorUniform.

rjbgoudie/structmcmc documentation built on Nov. 3, 2020, 3:41 a.m.