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Understanding Bayesian Experimental Design with Its Applications

베이지안 실험계획법의 이해와 응용

Lee, Gunhee
이군희

  • Received : 2014.09.16
  • Accepted : 2014.10.27
  • Published : 2014.12.31

Abstract

Bayesian experimental design is a useful concept in applied statistics for the design of efficient experiments especially if prior knowledge in the experiment is available. However, a theoretical or numerical approach is not simple to implement. We review the concept of a Bayesian experiment approach for linear and nonlinear statistical models. We investigate relationships between prior knowledge and optimal design to identify Bayesian experimental design process characteristics. A balanced design is important if we do not have prior knowledge; however, prior knowledge is important in design and expert opinions should reflect an efficient analysis. Care should be taken if we set a small sample size with a vague improper prior since both Bayesian design and non-Bayesian design provide incorrect solutions.

Keywords

Experimental design;Bayesian method;Bayesian decision theory;Monte-Carlo method

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