hc {bnlearn}R Documentation

Hill-Climbing (HC) learning algorithm

Description

Learn the structure of a Bayesian network using a hill-climbing (HC) greedy search.

Usage

hc(x, start = NULL, whitelist = NULL, blacklist = NULL,
    score = NULL, ..., debug = FALSE, restart = 0,
    perturb = 1, max.iter = Inf, optimized = TRUE)

Arguments

x a data frame, containing the variables in the model.
start an object of class bn, the preseeded directed acyclic graph used to initialize the algorithm. If none is specified, an empty one (i.e. without any arc) is used.
whitelist a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.
blacklist a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.
score a character string, the label of the network score to be used in the algorithm. If none is specified, the default score is the Akaike Information Criterion for discrete data sets and the Bayesian Gaussian posterior density for continuous ones. See bnlearn-package for details.
... additional tuning parameters for the network score. See score for details.
debug a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.
restart an integer, the number of random restarts.
perturb an integer, the number of attempts to randomly insert/remove/reverse an arc on every random restart.
max.iter an integer, the maximum number of iterations.
optimized a boolean value. See bnlearn-package for details.

Value

An object of class bn. See bn-class for details.

Author(s)

Marco Scutari

References

K. Korb and A. Nicholson. Bayesian artificial intelligence. Chapman and Hall, 2004.

D. Margaritis. Learning Bayesian Network Model Structure from Data. PhD thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, May 2003. Available as Technical Report CMU-CS-03-153.

R. Daly and Q. Shen. Methods to accelerate the learning of Bayesian network structures. Proceedings of the 2007 UK Workshop on Computational Intelligence.

See Also

gs, iamb, fast.iamb, inter.iamb, mmpc.


[Package bnlearn version 1.3 Index]