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makesimprob

Make a suggestion for simulation probabilities


Description

Creates local probability distributions reflecting the graph of the network. These are attached as a simprob property to each node in the network and can be edited and used for rnetwork.

Usage

makesimprob(nw,
                        s2=function(idx,cf) {
                          cf <- as.vector(cf)
                          xs <- (1:length(cf))
                          log(xs%*%cf+1)
                        },
                        m0=function(idx,cf) {
                          cf <- as.vector(cf)
                          xs <- (1:length(cf))^2
                          .69*(xs%*%cf)
                          },
                        m1=function(idx,cf) {
                          cf <- as.vector(cf)
                          xs <- (1:length(cf))*10
                          idx*(cf%*%xs)
                          })

Arguments

nw

an object of class network.

s2

function that returns the variance as a function of the node index and the configuration of the discrete variables.

m0

function that returns the intercept as a function of the node index and the configuration of the discrete variables.

m1

function that returns the regression coefficients as a function of the node index and the configuration of the discrete variables.

Details

For each node, the local simprob is determined. If the node is discrete, the probability distribution is uniform (and thus not reflecting the dependence in the graph, as it should). If the node is continuous, one mean and variance is attached per configuration of the discrete parents. The mean depends on the continuos parents and is the regression coefficients determined by the functions m0 (intercept) and m1 (regression coefficients). The variance is determined by the function s2.

Value

The network object nw, where each node has attached the property simprob.

Author(s)

Susanne Gammelgaard Bottcher,
Claus Dethlefsen rpackage.deal@gmail.com.

See Also


deal

Learning Bayesian Networks with Mixed Variables

v1.2-39
GPL (>= 2)
Authors
Susanne Gammelgaard Bottcher, Claus Dethlefsen.
Initial release
2018-10-20

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