Stochastic optimization with structured distributions:The case of Bayesian nets

A. Gaivoronski and F. Stella

Annals of Operations Research, 1998, vol. 81, issue 0, 189-212

Abstract: In this paper, the authors consider interworking between statistical procedures for recoveringthe distribution of random parameters from observations and stochastic programmingtechniques, in particular stochastic gradient (quasigradient) methods. The proposed problemformulation is based upon a class of statistical models known as Bayesian nets. The reasonfor the latter choice is that Bayesian nets are powerful and general statistical models emergedrecently within the more general framework of Bayesian statistics, which is specificallydesigned for cases when the vector of random parameters can have considerable dimensionandyor it is difficult to come up with traditional parametric models of the joint distribution ofrandom parameters. We define the optimization problem on a Bayesian net. For the solutionof this problem, we develop algorithms for sensitivity analysis of such a net and presentcombined optimization and sampling techniques. Copyright Kluwer Academic Publishers 1998

Date: 1998
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1018948905888 (text/html)
Access to full text is restricted to subscribers.

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:annopr:v:81:y:1998:i:0:p:189-212:10.1023/a:1018948905888

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1023/A:1018948905888

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().