Sequential Sampling to Myopically Maximize the Expected Value of Information

@article{Chick2010SequentialST,
  title={Sequential Sampling to Myopically Maximize the Expected Value of Information},
  author={Stephen E. Chick and Juergen Branke and Christian Schmidt},
  journal={INFORMS Journal on Computing},
  year={2010},
  volume={22},
  pages={71-80}
}
Statistical selection procedures are used to select the best of a finite set of alternatives, where “best” is defined in terms of each alternative’s unknown expected value, and the expected values are inferred through statistical sampling. One effective approach, which is based on a Bayesian probability model for the unknown mean performance of each alternative, allocates samples based on maximizing an approximation to the expected value of information (EVI) from those samples. The… CONTINUE READING
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