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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, maxp = Inf, optimized = TRUE) tabu(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ..., debug = FALSE, tabu = 10, max.tabu = tabu, max.iter = Inf, maxp = Inf, optimized = TRUE)

### Arguments

`x` |
a data frame containing the variables in the model. |

`start` |
an object of class |

`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 |

`...` |
additional tuning parameters for the network score. See |

`debug` |
a boolean value. If |

`restart` |
an integer, the number of random restarts. |

`tabu` |
a positive integer number, the length of the tabu list used in the |

`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. |

`maxp` |
the maximum number of parents for a node. The default value is |

`optimized` |
a boolean value. See |

### 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 (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, alpha.star.

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