Advertisement

Bayesian Approach to Continuous Global and Stochastic Optimization

  • Jonas Mockus
  • William Eddy
  • Audris Mockus
  • Linas Mockus
  • Gintaras Reklaitis
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 17)

Abstract

Consider a family C A of continuous functions f = f(x), x ∈ A ⊂ R m . Assume a possibility to evaluate f at any fixed point x n , n = 1,..., N, where N is the total number of observations.

Keywords

Global Minimum Bayesian Method Conditional Expectation Risk Function Wiener Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Jonas Mockus
    • 1
    • 2
    • 3
  • William Eddy
    • 4
  • Audris Mockus
    • 5
  • Linas Mockus
    • 6
  • Gintaras Reklaitis
    • 6
  1. 1.Institute of Mathematics and InformaticsKaunas Technological UniversityVilniusLithuania
  2. 2.Vytautas Magnus UniversityVilniusLithuania
  3. 3.Vilnius Technical UniversityVilniusLithuania
  4. 4.Department of StatisticsCarnegie-Mellon UniversityPittsburghUSA
  5. 5.Lucent Technologies AT&T Bell LaboratoriesPittsburghUSA
  6. 6.School of Chemical EngineeringPurdue UniversityW. LafayetteUSA

Personalised recommendations