Description Usage Arguments Details Value Author(s) References Examples

View source: R/APFAfunctions.R

The Kullback-Leibler divergence measures the similarity between two APFA models. If the two models are identical then it is zero.

1 | ```
KL(A,B)
``` |

`A` |
APFA igraph object |

`B` |
APFA igraph object |

A and B must be commensurate, i.e., defined on the same variable set. Note that the KL-divergence is not a true distance measure, since it is not not symmetric in A and B. For large APFA the computation of the KL-divergence may be prohibitive in both time and memory.

Returns the KL-divergence.

Smitha Akinakatte and David Edwards

Thollard, F.; Dupont, P. & de la Higuera, C. Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality 17th International Conference on Machine Learning., 2000, 975-982

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