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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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 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;
 Language
Korean
 Cited by
 References
1.
Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis, Springer, New York.

2.
Box, G. E. P. and Tiao, G. C.(1973). Bayesian Inference in Statistical Analysis, Addison-Wesley, Reading, MA.

3.
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)

4.
Chaloner, K. and Verdinellli, I.(1995). Bayesian experimental design: A review, Statistical Science, 10, 273-304. crossref(new window)

5.
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)

6.
Lindley, D. V. (1972). Bayesian Statistics-A Review, SIAM, Philadelphia.

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

8.
Raiffa, H. and Schlaifer, R. (1961). Applied Statistical Decision Theory, Harvard Business School, Boston.

9.
Shannon, C. E. (1948). A mathematical theory and communication, Bell System Technology Journal, 27, 379-423, 623-656. crossref(new window)

10.
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)

11.
Tanner, M. A. (1998). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, Springer, New York.

12.
Tsutakawa, R. K. (1972). Design of experiment for bioassay, Journal of the American Statistical Association, 67, 584-590. crossref(new window)