hc {bnlearn} | R Documentation |
Learn the structure of a Bayesian network using a hill-climbing (HC) greedy search.
hc(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ..., debug = FALSE, optimized = TRUE)
x |
a data frame, containing the variables in the model. |
start |
an object of class bn , the preseeded network
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 (Akaike Information Criterion),
bic (Bayesian Information Criterion), k2
(K2 score), bde or dir (Bayesian
dirichlet posterior density), bge (Bayesian
gaussian posterior density). If none is specified, the default
score is the Akaike Information Criterion for discrete
data sets and the Bayesian gaussian posterior density
for discrete 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. |
optimized |
a boolean value. See bnlearn-package
for details. |
An object of class bn
.
See bn-class
for details.
Marco Scutari
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.
gs
, iamb
, fast.iamb
,
inter.iamb
.