hybrid algorithms {bnlearn} R Documentation

Hybrid structure learning algorithms

Description

Learn the structure of a Bayesian network with the Max-Min Hill Climbing (MMHC) and the more general 2-phase Restricted Maximization (RSMAX2) hybrid algorithms.

Usage

rsmax2(x, whitelist = NULL, blacklist = NULL, restrict = "si.hiton.pc",
  maximize = "hc", restrict.args = list(), maximize.args = list(), debug = FALSE)
mmhc(x, whitelist = NULL, blacklist = NULL, restrict.args = list(),
  maximize.args = list(), debug = FALSE)

Arguments

x

a data frame containing the variables in the model.

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.

restrict

a character string, the constraint-based or local search algorithm to be used in the “restrict” phase. See bnlearn-package and the documentation of each algorithm for details.

maximize

a character string, the score-based algorithm to be used in the “maximize” phase. Possible values are hc and tabu. See bnlearn-package for details.

restrict.args

a list of arguments to be passed to the algorithm specified by restrict, such as test or alpha.

maximize.args

a list of arguments to be passed to the algorithm specified by maximize, such as restart for hill-climbing or tabu for tabu search.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Value

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

Note

mmhc() is simply rsmax2() with restrict set to mmpc and maximize set to hc.

Author(s)

Marco Scutari

References

Tsamardinos I, Brown LE, Aliferis CF (2006). "The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm". Machine Learning, 65(1), 31-78.

See Also

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