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, restart = 0, perturb = 1, max.iter = Inf, optimized = TRUE)
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. |
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
, mmpc
.