.png){kind=icon}
Return
Stochastic optimization on Bayesian nets
.png){kind=icon}
PRE
AI
DOI：10.1016/S0377-2217(96)00403-1.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we are concerned with stochastic optimization problems in the case when the joint probability distribution, associated with random parameters, can be described by means of a Bayesian net, In such a case we suggest that the structured nature of the probability distribution can be exploited for designing efficient gradient estimation algorithm. Such gradient estimates can be used within the general framework of stochastic gradient (quasi-gradient) solution procedures in order to solve complex non-linear stochastic optimization problems. We describe a gradient estimation algorithm and present a case study related to the reliability of semiconductor manufacturing together with numerical experiments. (C) 1997 Published by Elsevier Science B.V.
Keywords:
stochastic programming
stochastic gradient methods
optimization
Bayesian nets
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
No organization information available
Cited Papers
Energy detection based cooperative spectrum sensing using fuzzy conditional entropy maximization
.png){kind=icon}
PREAI
ShareSave
ShareSave no more
no more