Estimating Expected Information Gains for Experimental Designs With Application to the Random Fatigue-Limit Model
By K. J. Ryan. 2003
196 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fb169ea112c76103fdaabe3f27b0b8790ea8cc90
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Management Science and Operations Research
In Statistics and Probability
- Xun Huan
- James M. McGree
- Elizabeth G. Ryan
- Anthony N. Pettitt
- C. Drovandi
- Youssef M. Marzouk
- Mark A. Girolami
- Liang Yan
Citations in the corpus
- A Review of Modern Computational Algorithms for Bayesian Optimal Design (2016, methodology): “Ryan (2003) used mutual information to find static designs for efficient parameter estimation.”
- Simulation-based optimal Bayesian experimental design for nonlinear systems (2011, methodology): “The bias is proportional to C(d)/nin [14].”