TITLE

A BAYESIAN ALGORITHM FOR GLOBAL OPTIMIZATION

AUTHOR(S)
Betró, B.; Rotondi, R.
PUB. DATE
August 1984
SOURCE
Annals of Operations Research;1984, Vol. 1 Issue 1-4, p111
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
A crucial step in global optimization algorithms based on random sampling in the search domain is decision about the achievement of a prescribed accuracy. In order to overcome the difficulties related to such a decision, the Bayesian Nonparametric Approach has been introduced. The aim of this paper is to show the effectiveness of the approach when an ad hoc clustering technique is used for obtaining promising starting points for a local search algorithm. Several test problems are considered.
ACCESSION #
18641900

 

Related Articles

  • An adaptive stochastic global optimization algorithm for one-dimensional functions. Locatelli, Marco; Schoen, Fabio // Annals of Operations Research;1995, Vol. 58 Issue 1-4, p263 

    In this paper a new algorithm is proposed, based upon the idea of modeling the objective function of a global optimization problem as a sample path from a Wiener process. Unlike previous work in this field, in the proposed model the parameter of the Wiener process is considered as a random...

  • Stochastic optimization with structured distributions: The case of Bayesian nets. Gaivoronski, A.; Stella, F. // Annals of Operations Research;1998, Vol. 81 Issue 1-4, p189 

    In this paper, the authors consider interworking between statistical procedures for recovering the distribution of random parameters from observations and stochastic programming techniques, in particular stochastic gradient (quasigradient) methods. The proposed problem formulation is based upon...

  • VALUATION-BASED SYSTEMS FOR BAYESIAN DECISION ANALYSIS. Shenoy, Prakash P. // Operations Research;May/Jun92, Vol. 40 Issue 3, p463 

    This paper proposes a new method for representing and solving Bayesian decision problems. The representation is called a valuation-based system and has some similarities to influence diagrams. However, unlike influence diagrams which emphasize conditional independence among random variables,...

  • SIZE-BIASED DISTRIBUTIONS AND THEIR APPLICATIONS. Ahmad Mir, Khurshid; Ahmad, Munir // Pakistan Journal of Statistics;2009, Vol. 25 Issue 3, p283 

    In this paper, some size-biased probability distributions and their generalizations have been introduced. These distributions provide a unifying approach for the problems where the observations fall in the non-experimental, non- replicated, and nonrandom categories. These distributions take into...

  • BLAU'S DILEMMA REVISITED. Nau, Robert F. // Management Science;Oct87, Vol. 33 Issue 10, p1232 

    The issue of equivalence between chance-constrained programming problems (CCPP's) and Bayesian utility-maximization problems (BUMP's), and the anomalous evaluation of information in CCPP's, are re-examined in light of a recent paper by Jagannathan and the ensuing exchange between Jagannathan and...

  • On cautious probabilistic inference and default detachment. Thöne, Helmut; Kießling, Werner; Güntzer, Ulrich // Annals of Operations Research;1995, Vol. 55 Issue 1-4, p195 

    Conditional probabilities are one promising and widely used approach to model uncertainty in information systems. This paper discusses the DUCK-calculus, which is founded on the cautious approach to uncertain probabilistic inference. Based on a set of sound inference rules, derived probabilistic...

  • Three case studies in the Bayesian analysis of cognitive models.  // Psychonomic Bulletin & Review;Feb2008, Vol. 15 Issue 1, p1 

    Bayesian statistical inference offers a principled and comprehensive approach for relating psychological models to data. This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of...

  • THE CONSENSUS OF SUBJECTIVE PROBABILITY DISTRIBUTIONS. Winkler, Robert L. // Management Science;Oct68, Vol. 15 Issue 2, pB-61 

    "But we can't agree whether A or B is correct,' he concluded, 'and so we're collecting expert opinions, weighting them appropriately, and programming WESCAC to arbitrate the whole question.' " (John Barth, Giles Goat-Boy, p. 664.) In the Bayesian framework, quantified judgments about uncertainty...

  • ON JUSTIFICATION OF USE OF STOCHASTIC FUNCTIONS FOR MULTIMODAL OPTIMIZATION MODELS. Žilinskas, A. // Annals of Operations Research;1984, Vol. 1 Issue 1-4, p129 

    A model of a complicated function under uncertainty is constructed axiomatically, formalizing suppositions on rationality of information on a considered function.

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics