bnlearn - Bayesian network structure learning

score-based algorithms {bnlearn}

R Documentation

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 `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.
`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.

Author(s)

Marco Scutari

References

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

Korb K, Nicholson AE (2003). Bayesian Artificial Intelligence. Chapman & Hall/CRC.

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.