bnlearn (version 0.8)

hc: 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, 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. Possible values are lik (multinomial likelihood), loglik (multinomial loglikelihood), aic (Akaik

...

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.

optimized

a boolean value. See bnlearn-package for details.

Value

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

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.