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Understanding Bayesian Experimental Design with Its Applications
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 Title & Authors
Understanding Bayesian Experimental Design with Its Applications
Lee, Gunhee;
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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;
 Cited by
Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis, Springer, New York.

Box, G. E. P. and Tiao, G. C.(1973). Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading, MA.

Chaloner, K. and Larntz, K.(1989). Optimal Bayesian design applied to logistic regression experiments, Journal of Statistical Planning and Inference, 21, 191-208. crossref(new window)

Chaloner, K. and Verdinellli, I.(1995). Bayesian experimental design: A review, Statistical Science, 10, 273-304. crossref(new window)

Huan, X. and Marzouk, Y. M.(2013). Simulation-based optimal Bayesian experimental design for nonlinear systems, Journal of Computational Physics, 232, 288-317. crossref(new window)

Lindley, D. V. (1972). Bayesian Statistics-A Review, SIAM, Philadelphia.

Nelder, J. A. and Mead, R. (1965). A Simplex method for function minimization, Computer Journal, 7, 308-313. crossref(new window)

Raiffa, H. and Schlaifer, R. (1961). Applied Statistical Decision Theory, Harvard Business School, Boston.

Shannon, C. E. (1948). A mathematical theory and communication, Bell System Technology Journal, 27, 379-423, 623-656. crossref(new window)

Sun, D., Tsutakawa, R. K. and Lu, W. S. (1996). Bayesian design of experiment for quantal response: What is promised versus what is delivered, Journal of Statistical Planning and Inference, 52, 289-306. crossref(new window)

Tanner, M. A. (1998). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, Springer, New York.

Tsutakawa, R. K. (1972). Design of experiment for bioassay, Journal of the American Statistical Association, 67, 584-590. crossref(new window)