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Dynamic Programming Inference of Markov Networks from Finite Sets of Sample Strings

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2 Author(s)
Michael G. Thomason ; Department of Computer Science, University of Tennessee, Knoxville, TN 37996. ; Erik Granum

Inference of Markov networks from finite sets of sample strings is formulated using dynamic programming. Strings are installed in a network sequentially via optimal string-to-network alignments computed with a dynamic programming matrix, the cost function of which uses relative frequency estimates of transition probabilities to emphasize landmark substrings common to the sample set. Properties of an inferred network are described and the method is illustrated with artificial data and with data representing banded human chromosomes.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-8 ,  Issue: 4 )