Probabilistic Inference Using Function Factorization and Divergence Minimization

Terence H. Chan () and Raymond W. Yeung ()
Additional contact information
Terence H. Chan: University of South Australia, Institute for Telecommunications Research
Raymond W. Yeung: The Chinese University of Hong Kong, Department of Information Engineering

Chapter Chapter 3 in Towards an Information Theory of Complex Networks, 2011, pp 47-74 from Springer

Abstract: 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.

Keywords: Divergence distance; Factorization; Hammersley–Clifford theorem; Markov random field; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-8176-4904-3_3

Ordering information: This item can be ordered from
http://www.springer.com/9780817649043

DOI: 10.1007/978-0-8176-4904-3_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().