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In decision theory, the expected value of sample information (EVSI) is the expected increase in utility that you could obtain from gaining access to a sample of additional observations before making a decision. The additional information obtained from the sample may allow you to make a more informed, and thus better, decision, thus resulting in an increase in expected utility. EVSI attempts to estimate what this improvement would be before seeing actual sample data; hence, EVSI is a form of what is known as ''preposterior analysis''. == Formulation == Let : It is common (but not essential) in EVSI scenarios for , and , which is to say that each observation is an unbiased sensor reading of the underlying state , with each sensor reading being independent and identically distributed. The utility from the optimal decision based only on your prior, without making any further observations, is given by : If you could gain access to a single sample, , the optimal posterior utility would be: : where is obtained from Bayes' rule: : : Since you don't know what sample would actually be obtained if you were to obtain a sample, you must average over all possible samples to obtain the expected utility given a sample: : The expected value of sample information is then defined as: : 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「expected value of sample information」の詳細全文を読む スポンサード リンク
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