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Probabilistic Programming discusses a high-level language known as probabilistic programming.
This book consists of three chapters. Chapter I deals with “wait-and-see” problems that require waiting until an observation is made on the random elements, while Chapter II contains the analysis of decision problems, particularly of so-called two-stage problems. The last chapter focuses on “chance constraints,” such as constraints that are not expected to be always satisfied, but only in a proportion of cases or “with given probabilities.”
This text specifically deliberates the decision regions for optimality, probability distributions, Kall's Theorem, and two-stage programming under uncertainty. The complete problem, active approach, quantile rules, randomized decisions, and nonzero order rules are also covered.
This publication is suitable for developers aiming to define and automatically solve probability models.
I. Stochastic Programming
Feasibility and Convexity
Optimality and Convexity
Decision Regions for Optimality
II. Decision Problems
A Decision Problem
The Active Approach
Two-Stage Programming Under Uncertainty
The Complete Problem
Discrete Values of bi
The General Case, b Stochastic
The General Case, A and b Stochastic
The General Case, b, A, and B Stochastic
III. Chance Constraints
Nonzero Order Rules
Linear Programming and Duality
Applications of Stochastic (Probabilistic) Programming in Various Fields (References)
- No. of pages:
- © Academic Press 1972
- 28th January 1972
- Academic Press
- eBook ISBN:
Steven Vajda, Visiting Professor at Sussex University, formerly Professor of Operational Research, Department of Engineering Production, University of Birmingham.
Mount Sinai School of Medicine, New York, NY, USA
Bowling Green State University