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Computational Statistics and Data Analysis, ISSN 0167-9473, 11/2017, Volume 115, pp. 79 - 90
Screening designs are frequently used in the initial stages of experimentation with the goal of identifying important main effects as well as to gain insight... 
Bayesian DP-optimal | Primary terms | Model misspecification | Potential terms | Pure error | Partial replication | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | FRACTIONAL FACTORIAL-DESIGNS | 2-LEVEL | STATISTICS & PROBABILITY | SUPERSATURATED DESIGNS | SELECTION
Journal Article
International Journal of Information and Decision Sciences, ISSN 1756-7017, 2011, Volume 3, Issue 3, pp. 203 - 227
This paper explores the validity and usefulness of dynamic decision networks (DDNs) in approximating dynamic programming (DP). An approach for comparing the... 
Bayesian updating | dynamic decision networks | influence diagrams | decision sciences | optimal decisions | BNs | decision trees | dynamic programming | sequential decision-making | Bayesian networks | Networks | Approximation | Policies | Computation | Dynamics | Mathematical analysis | Dynamic programming | Optimization
Journal Article
by Hill, Steven M and Heiser, Laura M and Cokelaer, Thomas and Linger, Michael and Nesser, Nicole K and Carlin, Daniel E and Zhang, Yang and Sokolov, Artem and Paull, Evan O and Wong, Chris K and Graim, Kiley and Bivol, Adrian and Wang, Haizhou and Zhu, Fan and Afsari, Bahman and Danilova, Ludmila V and Favorov, Alexander V and Lee, Wai Shing and Taylor, Dane and Hu, Chenyue W and Long, Byron L and Noren, David P and Bisberg, Alexander J and Mills, Gordon B and Gray, Joe W and Kellen, Michael and Norman, Thea and Friend, Stephen and Qutub, Amina A and Fertig, Elana J and Guan, Yuanfang and Song, Mingzhou and Stuart, Joshua M and Spellman, Paul T and Koeppl, Heinz and Stolovitzky, Gustavo and Saez-Rodriguez, Julio and Mukherjee, Sach and Al-Ouran, Rami and Anton, Bernat and Arodz, Tomasz and Sichani, Omid Askari and Bagheri, Neda and Berlow, Noah and Bohler, Anwesha and Bonet, Jaume and Bonneau, Richard and Budak, Gungor and Bunescu, Razvan and Caglar, Mehmet and Cai, Binghuang and Cai, Chunhui and Carlon, Azzurra and Chen, Lujia and Ciaccio, Mark F and Cooper, Gregory and Coort, Susan and Creighton, Chad J and Daneshmand, Seyed-Mohammad-Hadi and De La Fuente, Alberto and Di Camillo, Barbara and Dutta-Moscato, Joyeeta and Emmett, Kevin and Evelo, Chris and Fassia, Mohammad-Kasim H and Finotello, Francesca and Finkle, Justin D and Gao, Xi and Garcia-Garcia, Javier and Ghosh, Samik and Giaretta, Alberto and Großeholz, Ruth and Guinney, Justin and Hafemeister, Christoph and Hahn, Oliver and Haider, Saad and Hase, Takeshi and Hodgson, Jay and Hoff, Bruce and Hsu, Chih Hao and Hu, Ying and Huang, Xun and Jalili, Mahdi and Jiang, Xia and Kacprowski, Tim and Kaderali, Lars and Kang, Mingon and Kannan, Venkateshan and Kikuchi, Kaito and Kim, Dong-Chul and Kitano, Hiroaki and Knapp, Bettina and Komatsoulis, George and Krämer, Andreas and Kursa, Miron Bartosz and Kutmon, Martina and Li, Yichao and Liang, Xiaoyu and Liu, Zhaoqi and Liu, Yu and ... and HPN-DREAM Consortium and The HPN-DREAM Consortium
Nature Methods, ISSN 1548-7091, 03/2016, Volume 13, Issue 4, pp. 310 - 322
Journal Article
Journal of Computational and Graphical Statistics, ISSN 1061-8600, 10/2019, Volume 28, Issue 4, pp. 861 - 876
Finding Bayesian optimal designs for nonlinear models is a difficult task because the optimality criterion typically requires us to evaluate complex integrals... 
Equivalence theorem | P-optimality | Compound criterion | Adaptive subregion algorithm | D-efficiency | Fisher information matrix | ROBUST | MINIMAX OPTIMAL DESIGNS | STATISTICS & PROBABILITY | IMPERIALIST COMPETITIVE ALGORITHM
Journal Article
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