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Probabilistic Inference Using Function Factorization and Divergence Minimization

In: Towards an Information Theory of Complex Networks

Author

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  • Terence H. Chan

    (University of South Australia, Institute for Telecommunications Research)

  • Raymond W. Yeung

    (The Chinese University of Hong Kong, Department of Information Engineering)

Abstract

This chapter addresses modeling issues in statistical inference problems. We will focus specifically on factorization model which is a generalization of Markov random fields and Bayesian networks. For any positive function (say an estimated probability distribution), we present a mechanical approach which approximates the function with one in a factorization model that is as simple as possible, subject to an upper bound on approximation error. We also rewrite a probabilistic inference problem into a divergence minimization (DM) problem where iterative algorithms are proposed to solve the DM problem. We prove that the well-known EM algorithm is a special case of our proposed iterative algorithm.

Suggested Citation

  • Terence H. Chan & Raymond W. Yeung, 2011. "Probabilistic Inference Using Function Factorization and Divergence Minimization," Springer Books, in: Matthias Dehmer & Frank Emmert-Streib & Alexander Mehler (ed.), Towards an Information Theory of Complex Networks, edition 1, chapter 0, pages 47-74, Springer.
    • Handle: RePEc:spr:sprchp:978-0-8176-4904-3_3
    DOI: 10.1007/978-0-8176-4904-3_3
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