maketrylist: Creates the full trylist

In ClausDethlefsen/deal: Learning Bayesian Networks with Mixed Variables

Creates the full trylist

Description

For faster learning, a trylist is maintained as a lookup table for a given parent configuration of a node.

Usage

maketrylist(initnw,data,prior=jointprior(network(data)),timetrace=FALSE) 

Arguments

initnw	an object of class network, from which the search is started.
data	a data frame used for learning the network, see network.
prior	a list containing parameter priors, generated by jointprior.
timetrace	a logical. If TRUE, prints some timing information on the screen.

Details

This procedure is included for illustrative purposes. For each node in the network, all possible parent configurations are created and learned. The result is called a trylist. To create the full trylist is very time-consuming, and a better choice is to maintain a trylist while searching and indeed this is automatically done. The trylist is given as output to all functions that call the learning procedure and can be given as an argument.

Value

A list with one element per node in the network. In the list, element i is a matrix with two columns: a string with the indices of the parent nodes, separated by ":", and a numeric with the log-likelihood contribution of the node given the parent configuration. Whenever learning is performed of a node given a parent configuration, the trylist is consulted to yield faster learning, especially useful when using autosearch or heuristic.

Author(s)

Susanne Gammelgaard Bottcher,
Claus Dethlefsen [e-mail].

See Also

networkfamily, autosearch heuristic

Examples

data(rats)
rats.nw <- network(rats)
rats.pr <- jointprior(rats.nw,12)
rats.nw <- getnetwork(learn(rats.nw,rats,rats.pr))
rats.tr <- maketrylist(rats.nw,rats,rats.pr)

rats.hi <- getnetwork(heuristic(rats.nw,rats,rats.pr,trylist=rats.tr))

ClausDethlefsen/deal documentation built on Nov. 18, 2022, 11:38 p.m.