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An efficient toolkit for exact and approximate inference in large scale graphical models.

Saurabh Pratap Singh

Abstract


Graphical models have many real life applications in diverse fields like gene regulatory networks,speech recognition, gene finding, computer vision and diagnosis of diseases. This is because of the natural ability, that graphical models offer, to represent probabilistic interactions in different scenarios.Major problems of real life comes in two type of problems on graphical models marginal probabilities given some evidence i.e. inference problem and maximum probable assignment problem (MAP).
In real life applications, graphs are not simple structures like polytrees but multiply connected graphs and often have a large number of variables or a large number of possible labels or lots of interconnections.Exact inference on such graphs is exponential in the largest cliques size in the moralized graph induced tree width. So in the case of dense graph, with many possible labels, this problem becomes intractable.In this work I focused on MAP problems, which arise in the field of information extraction. In the information extraction field, in general, models have some properties in their potentials. I developed a new version of the Viterbi algorithm which works faster than the original Viterbi algorithm by exploiting these properties in the potentials.








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