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最优探索的一类贝叶斯模型AI 解读

SUMMARY Each of several locations contains an unknown number of objects of value. A single search of a location will discover some of these objects which are then removed. Discoveries yield rewards. A ‘distribution of effort’ problem is posed concerning how to explore the locations optimally—i.e. to maximize the expected return from discoveries made. This is formulated as a Bayes sequential decision problem for which index policies are optimal. A natural simple case is one…

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二元响应筛查的稳健设计AI 解读

Optimal screening procedures are derived using Bayesian decision theory. A simple heuristic solution is determined using an asymptotic posterior distribution for the parameters in a linear probit model. The solution is shown to be optimal within a class of possible procedures and robust to departures from model assumptions.

序贯实验设计中的随机化动态分配指数AI 解读

Summary In response to a suggestion by Bather, Gittins proposed the adoption of rules for sampling one at a time from several Bernoulli populations which were based on randomized versions of his dynamic allocation indices. Such rules, Gittins conjectured, might sometimes be both asymptotically optimal and also (almost) Bayes with respect to the usual discounted reward criterion. This conjecture is investigated and verified.