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Technometrics Volume 29, 1987 - Issue 4
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Original Articles

Bayesian Control in Mixture Models

L. Mark Berliner Department of Statistics, Ohio State University, Columbus, OH, 43210
Pages 455-460 | Published online: 23 Mar 2012

Citations (14)

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IRAD BEN-GAL & MICHAEL CARAMANIS. (2002) Sequential DOE via dynamic programming. IIE Transactions 34:12, pages 1087-1100.
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Michael Cain & RogerJ. Owen. (1990) Regressor Levels for Bayesian Predictive Response. Journal of the American Statistical Association 85:409, pages 228-231.
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Donald Holbert & JeffreyC. Johnson. (1989) Using prior information in fisheries management: A comparison of classical and bayesian methods for estimating population parameters. Coastal Management 17:4, pages 333-347.
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Articles from other publishers (11)

John A. Cornell. 2011. A Primer on Experiments with Mixtures. A Primer on Experiments with Mixtures 299 315 .
Enrique González-Dávila, Roberto Dorta-Guerra & Josep Ginebra. (2007) Simulation-based designs for multiperiod control. Computational Statistics & Data Analysis 51:12, pages 6624-6641.
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John A. Cornell. 2002. Experiments with Mixtures. Experiments with Mixtures 589 603 .
Zhuo Meng & Yoh-Han Pao. (2000) Visualization and self-organization of multidimensional data through equalized orthogonal mapping. IEEE Transactions on Neural Networks 11:4, pages 1031-1038.
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Chuancai Liu & Chang-Yun Shen. (1998) Synthetic Optimization Approach of Combining Regional Gnided Order Principle and Biological Evolutive Strategies. IFAC Proceedings Volumes 31:29, pages 143-148.
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Steven B. Fairchild, Y. Cao, C. L Philip Chen & Steven R. LeClair. (1998) Monitoring and Control of Rugate Filter Fabrication Using the Orthogonal Functional Basis Neural Network. IFAC Proceedings Volumes 31:29, pages 95-100.
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Yoh-Han Pao & Zhuo Meng. (1996) A perspective on functional-link computing, dimension reduction and signal/image understanding. A perspective on functional-link computing, dimension reduction and signal/image understanding.
Michael Cain & Roger J. Owen. (1994) Mixture selection for maximal stochastic performance. Naval Research Logistics 41:5, pages 625-634.
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José M. Bernardo & Adrian F. M. Smith. 1994. Bayesian Theory. Bayesian Theory 489 554 .
N. Baba & Y. Mogami. (1994) Utilization of hierarchical structure stochastic automata for neural network learning. Utilization of hierarchical structure stochastic automata for neural network learning.
D. B. Fogel, L. J. Fogel & V. W. Porto. (1990) Evolving neural networks. Biological Cybernetics 63:6, pages 487-493.
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