Sequentiality and Adaptivity Gains in Active Hypothesis Testing
Sequentiality and Adaptivity Gains in Active Hypothesis Testing
复制标题
主动假设检验中的顺序性和适应性增益
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
T. Javidi
中科院分区：
文献类型：
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作者：
Mohammad Naghshvar;T. Javidi
考虑一个决策者，他负责收集观察结果，以便以快速的方式增强他对潜在感兴趣的现象的信息。决策者选择感知行为的策略可以根据以下两个因素进行分类：i)顺序与非顺序；Ii)适应性与非适应性。非顺序策略收集固定数量的观察样本，然后再做最终决策；而在顺序策略下，样本量最初是未知的，由观察结果决定。在自适应策略下，决策者依靠先前收集的样本来选择下一个感知动作；而在非适应性策略下，行动的选择与过去的观察结果无关。本文给出了每一类策略的性能界限。利用这些边界，描述了序列增益和自适应增益，即序列和自适应选择的增益。
Consider a decision maker who is responsible to collect observations so as to enhance his information in a speedy manner about an underlying phenomena of interest. The policies under which the decision maker selects sensing actions can be categorized based on the following two factors: i) sequential versus non-sequential; ii) adaptive versus non-adaptive. Non-sequential policies collect a fixed number of observation samples and make the final decision afterwards; while under sequential policies, the sample size is not known initially and is determined by the observation outcomes. Under adaptive policies, the decision maker relies on the previous collected samples to select the next sensing action; while under non-adaptive policies, the actions are selected independent of the past observation outcomes. In this paper, performance bounds are provided for the policies in each category. Using these bounds, sequentiality gain and adaptivity gain, i.e., the gains of sequential and adaptive selection of actions are characterized.