Economical experiments: Bayesian efficient experimental design
Economical experiments: Bayesian efficient experimental design
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DOI：
10.1007/bf01803953
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发表时间：
1996-01-01
影响因子：
0.6
通讯作者：
Palfrey, TR
中科院分区：
文献类型：
--
作者：
ElGamal, MA;Palfrey, TR
我们提出并实现了一个贝叶斯优化设计过程。我们的程序将策略行为的参数模型、游戏（实验设计）和行为参数的先验作为其原语。我们选择的实验设计，最大限度地从实验的信息。我们按顺序使用给定的设计和模型进行抽样，直到只有一个模型具有可行的后验赔率。一个模型在一个小的模型集合中后验概率低，与一个更大的模型集合相比，后验概率会更低，因此我们可以忽略它。该程序可以通过引入新模型并将其与前几轮实验中幸存下来的模型进行比较来依次使用。重点不是进行尽可能多的实验，而是在尽可能短的时间内选择实验设计来区分模型。我们用一个片面不完全信息的简单实验游戏来说明这个过程。
We propose and implement a Bayesian optimal design procedure. Our procedure takes as its primitives a class of parametric models of strategic behavior, a class of games (experimental designs), and priors on the behavioral parameters. We select the experimental design that maximizes the information from the experiment. We sequentially sample with the given design and models until only one of the models has viable posterior odds. A model which has low posterior odds in a small collection of models will have an even lower posterior odds when compared to a larger class, and hence we can dismiss it. The procedure can be used sequentially by introducing new models and comparing them to the models that survived earlier rounds of experiments. The emphasis is not on running as many experiments as possible, but rather on choosing experimental designs to distinguish between models in the shortest possible time period. We illustrate this procedure with a simple experimental game with one-sided incomplete information.