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Understanding Bayesian Experimental Design with Its Applications  

Lee, Gunhee (Graduate School of Business, Sogang University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.6, 2014 , pp. 1029-1038 More about this Journal
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.
Experimental design; Bayesian method; Bayesian decision theory; Monte-Carlo method;
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