bnlearn (version 3.1)

score-based algorithms: Score-based structure learning algorithms

Description

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

Usage

hc(x, start = NULL, whitelist = NULL, blacklist = NULL,
  score = NULL, ..., debug = FALSE, restart = 0,
  perturb = 1, max.iter = Inf, optimized = TRUE)
tabu(x, start = NULL, whitelist = NULL, blacklist = NULL,
  score = NULL, ..., debug = FALSE, tabu = 10, max.tabu = tabu,
  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 theBayesian Information Criterion for both discrete and continuous data sets. See

...

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.

tabu

a positive integer number, the length of the tabu list used in the tabu function.

max.tabu

a positive integer number, the iterations tabu search can perform without improving the best network score.

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.

References

Russell SJ, Norvig P (2009). Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd edition.

Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.

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

Daly R, Shen Q (2007). "Methods to Accelerate the Learning of Bayesian Network Structures". In "Proceedings of the 2007 UK Workshop on Computational Intelligence", Imperial College, London.

See Also

constraint-based algorithms, hybrid algorithms, local discovery algorithms.