Bayesian Design of Experiments
Bayesian design of experiments selects experimental runs by maximising a utility function — typically the expected information gain — computed over prior beliefs about model parameters. Unlike classical design, which optimizes algebraic criteria such as D-optimality under fixed assumptions, Bayesian DOE incorporates prior knowledge and uncertainty about the system, yielding designs that are optimal in expectation across all plausible parameter values.
Rekod sumber
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- Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304. · DOI 10.1214/ss/1177009939
- Ryan, E. G., Drovandi, C. C., McGree, J. M., & Pettitt, A. N. (2016). A Review of Modern Computational Algorithms for Bayesian Optimal Design. International Statistical Review, 84(1), 128–154. · DOI 10.1111/insr.12107
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