As one of the most important Artificial Intelligence (AI) branches, Reinforcement Learning (RL) has attracted increasing attention in recent years. RL is an interdisciplinary field of trialβandβerror learning and optimal control that promises to provide optimal solutions for decisionβmaking or contr
Optimal Sequentially Planned Decision Procedures
β Scribed by Norbert Schmitz (auth.), Norbert Schmitz (eds.)
- Publisher
- Springer-Verlag New York
- Year
- 1993
- Tongue
- English
- Leaves
- 221
- Series
- Lecture Notes in Statistics 79
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Learning from experience, making decisions on the basis of the available information, and proceeding step by step to a desired goal are fundamental behavioural qualities of human beings. Nevertheless, it was not until the early 1940's that such a statistical theory - namely Sequential Analysis - was created, which allows us to investigate this kind of behaviour in a precise manner. A. Wald's famous sequential probability ratio test (SPRT; see example (1.8Β» turned out to have an enormous influence on the development of this theory. On the one hand, Wald's fundamental monograph "Sequential Analysis" ([Wa]*) is essentially centered around this test. On the other hand, important properties of the SPRT - e.g. BayesΒ optimality, minimax-properties, "uniform" optimality with respect to expected sample sizes - gave rise to the development of a general statistical decision theory. As a conseΒ quence, the SPRT's played a dominating role in the further development of sequential analysis and, more generally, in theoretical statistics.
β¦ Table of Contents
Front Matter....Pages N1-x
Introduction....Pages 1-28
Optimal sequential sampling plans....Pages 29-57
Sequentially planned tests; sequentially planned probability ratio tests....Pages 58-104
Bayes-optimal sequentially planned decision procedures....Pages 105-126
Optimal sequentially planned tests under side conditions....Pages 127-163
Back Matter....Pages 164-213
β¦ Subjects
Probability Theory and Stochastic Processes
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