Bayesian Methods for Global Optimization in the Gaussian Case

  • Jonas Mockus
Part of the Mathematics and Its Applications book series (MASS, volume 37)

Abstract

The formula for the one-step Bayesian approach assuming the Gaussian distribution is from (2.5.1) and (2.5.5)
$$x_{n + 1} \in \arg \mathop {\min }\limits_{x \in A} (1/\sigma )\int {_{ - \infty }^\infty } \min \,(y,c)\,\exp \,(( - {1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-\nulldelimiterspace} 2})\,((y - \mu )/\sigma )^2 )dy.$$
(5.1.1)

Keywords

Local Search Global Minimum Bayesian Method Conditional Expectation Sample Path 
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

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • Jonas Mockus
    • 1
  1. 1.Academy of Sciences of the Lithuanian SSRInstitute of Mathematics and CyberneticsVilniusUSSR

Personalised recommendations