Relevant bibliographies by topics / Bayesian Optimal Design

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian Optimal Design'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Bayesian Optimal Design"

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Sankoh, Abdul J. "Bayesian optimal stratified sampling design." Communications in Statistics - Theory and Methods 21, no. 11 (1992): 3185–96. http://dx.doi.org/10.1080/03610929208830970.

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Khodja, M. R., M. D. Prange, and H. A. Djikpesse. "Guided Bayesian optimal experimental design." Inverse Problems 26, no. 5 (2010): 055008. http://dx.doi.org/10.1088/0266-5611/26/5/055008.

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Atherton, Juli, Benoit Charbonneau, David B. Wolfson, Lawrence Joseph, Xiaojie Zhou, and Alain C. Vandal. "Bayesian optimal design for changepoint problems." Canadian Journal of Statistics 37, no. 4 (2009): 495–513. http://dx.doi.org/10.1002/cjs.10037.

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Rekab, Kamel. "An asymptotic optimal design." Journal of Applied Mathematics and Stochastic Analysis 4, no. 4 (1991): 357–61. http://dx.doi.org/10.1155/s1048953391000266.

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The problem of designing an experiment to estimate the product of the means of two normal populations is considered. A Bayesian approach is adopted in which the product of the means is estimated by its posterior mean. A fully sequential design is proposed and shown to be asymptotically optimal.
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Amzal, Billy, Frédéric Y. Bois, Eric Parent, and Christian P. Robert. "Bayesian-Optimal Design via Interacting Particle Systems." Journal of the American Statistical Association 101, no. 474 (2006): 773–85. http://dx.doi.org/10.1198/016214505000001159.

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Anand, Farminder S., Jay H. Lee, and Matthew J. Realff. "Optimal decision-oriented Bayesian design of experiments." Journal of Process Control 20, no. 9 (2010): 1084–91. http://dx.doi.org/10.1016/j.jprocont.2010.06.011.

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Glickman, Mark E. "Bayesian locally optimal design of knockout tournaments." Journal of Statistical Planning and Inference 138, no. 7 (2008): 2117–27. http://dx.doi.org/10.1016/j.jspi.2007.09.007.

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Hartline, Jason D., and Brendan Lucier. "Non-Optimal Mechanism Design." American Economic Review 105, no. 10 (2015): 3102–24. http://dx.doi.org/10.1257/aer.20130712.

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The optimal allocation of resources in complex environments—like allocation of dynamic wireless spectrum, cloud computing services, and Internet advertising—is computationally challenging even given the true preferences of the participants. In the theory and practice of optimization in complex environments, a wide variety of special and general purpose algorithms have been developed; these algorithms produce outcomes that are satisfactory but not generally optimal or incentive compatible. This paper develops a very simple approach for converting any, potentially non-optimal, algorithm for opti
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Hennessy, Jonathan, and Mark Glickman. "Bayesian optimal design of fixed knockout tournament brackets." Journal of Quantitative Analysis in Sports 12, no. 1 (2016): 1–15. http://dx.doi.org/10.1515/jqas-2015-0033.

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AbstractWe present a methodology for finding globally optimal knockout tournament designs when partial information is known about the strengths of the players. Our approach involves maximizing an expected utility through a Bayesian optimal design framework. Given the prohibitive computational barriers connected with direct computation, we compute a Monte Carlo estimate of the expected utility for a fixed tournament bracket, and optimize the expected utility through simulated annealing. We demonstrate our method by optimizing the probability that the best player wins the tournament. We compare
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Alhorn, K., K. Schorning, and H. Dette. "Optimal designs for frequentist model averaging." Biometrika 106, no. 3 (2019): 665–82. http://dx.doi.org/10.1093/biomet/asz036.

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SummaryWe consider the problem of designing experiments for estimating a target parameter in regression analysis when there is uncertainty about the parametric form of the regression function. A new optimality criterion is proposed that chooses the experimental design to minimize the asymptotic mean squared error of the frequentist model averaging estimate. Necessary conditions for the optimal solution of a locally and Bayesian optimal design problem are established. The results are illustrated in several examples, and it is demonstrated that Bayesian optimal designs can yield a reduction of t
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Dissertations / Theses on the topic "Bayesian Optimal Design"

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Atherton, Juli. "Bayesian optimal design for changepoint problems." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102954.

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We consider optimal design for changepoint problems with particular attention paid to situations where the only possible change is in the mean. Optimal design for changepoint problems has only been addressed in an unpublished doctoral thesis, and in only one journal article, which was in a frequentist setting. The simplest situation we consider is that of a stochastic process that may undergo a, change at an unknown instant in some interval. The experimenter can take n measurements and is faced with one or more of the following optimal design problems: Where should these n observations be take
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Zhang, Ying. "Bayesian D-Optimal Design for Generalized Linear Models." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30147.

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Bayesian optimal designs have received increasing attention in recent years, especially in biomedical and clinical trials. Bayesian design procedures can utilize the available prior information of the unknown parameters so that a better design can be achieved. However, a difficulty in dealing with the Bayesian design is the lack of efficient computational methods. In this research, a hybrid computational method, which consists of the combination of a rough global optima search and a more precise local optima search, is proposed to efficiently search for the Bayesian D-optimal designs for multi
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Huan, Xun. "Numerical approaches for sequential Bayesian optimal experimental design." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101442.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 175-186).<br>Experimental data play a crucial role in developing and refining models of physical systems. Some experiments can be more valuable than others, however. Well-chosen experiments can save substantial resources, and hence optimal
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Montes, Diez Raquel. "Optimal design of two-stage screens : a Bayesian approach." Thesis, University of Nottingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342027.

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Feng, Chi S. M. Massachusetts Institute of Technology. "Optimal Bayesian experimental design in the presence of model error." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97790.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 87-90).<br>The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain in targeted subsets of model parameters, particularly in situations where experiments are costl
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Al-Jaralla, Reem Abdulla. "Optimal design for Bayesian linear hierarchical models with measurement error." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248202.

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Li, Fengyi S. M. Massachusetts Institute of Technology. "A combinatorial approach to goal-oriented optimal Bayesian experimental design." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122376.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 83-87).<br>Optimal experimental design plays an important role in science and engineering. In many situations, we have many observations but only few of them can be selected due to limited resources. We then need to decide which ones to select
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Ryan, Elizabeth G. "Contributions to Bayesian experimental design." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/79628/1/Elizabeth_Ryan_Thesis.pdf.

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This thesis progresses Bayesian experimental design by developing novel methodologies and extensions to existing algorithms. Through these advancements, this thesis provides solutions to several important and complex experimental design problems, many of which have applications in biology and medicine. This thesis consists of a series of published and submitted papers. In the first paper, we provide a comprehensive literature review on Bayesian design. In the second paper, we discuss methods which may be used to solve design problems in which one is interested in finding a large number of (nea
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Jarman, Ben. "Essays in optimal auction design." University of Sydney, 2008. http://hdl.handle.net/2123/4627.

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Doctor of Philosophy (Economics)<br>Auctions are an ancient economic institution. Since Vickrey (1961), the development of auction theory has lead to an extremely detailed description of the often desirable characteristics of these simple selling procedures, in the process explaining their enduring popularity. Given the pervasiveness of auctions, the question of how a seller should engineer the rules of these mechanisms to maximize her own profits is a central issue in the organization of markets. The seminal paper of Myerson (1981) shows that when facing buyers with Independent Private Values
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Huffman, Jennifer Wade. "Optimal Experimental Design for Poisson Impaired Reproduction Studies." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30751.

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Impaired reproduction studies with Poisson responses are among a growing class of toxicity studies in the biological and medical realm. In recent years, little effort has been focused on the development of efficient experimental designs for impaired reproduction studies. This research concentrates on two areas: 1) the use of Bayesian techniques to make single regressor designs robust to parameter misspecification and 2) the extension of design optimality methods to the k-regressor model. The standard Poisson model with log link is used. Bayesian designs with priors on the parameters are exp
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Books on the topic "Bayesian Optimal Design"

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McDowall, David, Richard McCleary, and Bradley J. Bartos. Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.001.0001.

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread a
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Book chapters on the topic "Bayesian Optimal Design"

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Farrow, Malcolm. "Optimal Experiment Design, Bayesian." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1234.

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Carlon, Andre G., Rafael H. Lopez, Leandro F. F. Miguel, and André J. Torii. "Bayesian Optimal Experimental Design." In Uncertainty Modeling: Fundamental Concepts and Models. Biblioteca Central da Universidade de Brasilia, 2022. http://dx.doi.org/10.4322/978-65-86503-88-3.c15.

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Chawla, Shuchi, Jason D. Hartline, Uday Rajan, and R. Ravi. "Bayesian Optimal No-Deficit Mechanism Design." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11944874_13.

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Argyris, Costas, and Costas Papadimitriou. "Bayesian Optimal Experimental Design Using Asymptotic Approximations." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54858-6_26.

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Li, Xiao-Yang. "Multi-objective Bayesian Optimal Design for Accelerated Degradation Testing." In International Series in Operations Research & Management Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89647-8_15.

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Hassler, Edgar, Douglas C. Montgomery, and Rachel T. Silvestrini. "Bayesian D-Optimal Design Issues for Binomial Generalized Linear Model Screening Designs." In Frontiers in Statistical Quality Control 11. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12355-4_20.

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Argyris, Costas, and Costas Papadimitriou. "A Bayesian Framework for Optimal Experimental Design in Structural Dynamics." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_26.

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Dette, H., and W. J. Studden. "A Geometric Solution of the Bayesian E-Optimal Design Problem." In Statistical Decision Theory and Related Topics V. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_13.

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Jiang, Hongyan, and Yichuan Zhao. "A Review of Bayesian Optimal Experimental Design on Different Models." In Emerging Topics in Statistics and Biostatistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72437-5_10.

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Rosenberger, W. F., L. M. Haines, and I. Perevozskaya. "Constrained Bayesian Optimal Designs for Phase I Clinical Trials: Continuous Dose Space." In mODa 6 — Advances in Model-Oriented Design and Analysis. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57576-1_25.

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Conference papers on the topic "Bayesian Optimal Design"

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Huan, Xun, and Youssef Marzouk. "Optimal Bayesian Experimental Design for Combustion Kinetics." In 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-513.

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Jaiswal, Prateek, and Harsha Honnappa. "Statistical Inference for Approximate Bayesian Optimal Design." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9383857.

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Kuck, Hendrik, Nando de Freitas, and Arnaud Doucet. "SMC Samplers for Bayesian Optimal Nonlinear Design." In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, 2006. http://dx.doi.org/10.1109/nsspw.2006.4378829.

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Catanach, Thomas. "Bayesian Optimal Experimental Design for Seismic Monitoring." In Proposed for presentation at the 2021 Annual Meeting Seismological Society of America held April 19-23, 2021. US DOE, 2021. http://dx.doi.org/10.2172/1859700.

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Wizsa, Uqwatul Alma, Utami Dyah Syafitri, and Aji Hamim Wigena. "The Bayesian D-Optimal Design In Mixture Experimental Design." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290470.

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Li, Xia, Jun Guo, Uri Rogers, and Hao Chen. "Asymptotic optimal quantizer design for distributed Bayesian estimation." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472370.

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Rodrigues, Diogo, Georgios Makrygiorgos, and Ali Mesbah. "Tractable Global Solutions to Bayesian Optimal Experiment Design." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9304226.

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Ruiz-Agundez, Igor, Yoseba K. Penya, and Pablo Garcia Bringas. "Optimal Bayesian network design for efficient Intrusion Detection." In 2010 3rd International Conference on Human System Interactions (HSI). IEEE, 2010. http://dx.doi.org/10.1109/hsi.2010.5514530.

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Keren, Carmit, Miriam Zacksenhouse, and Yakov Ben-Haim. "Info Gap Bayesian Classification." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59188.

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Optimal decision methods and most notably the Bayesian decision are sensitive to uncertainty in the statistics of the patterns to be classified. Errors in the associated probabilities and distributions would degrade the performance of these methods. We present here a robust-satisficing decision-rule whose robustness to uncertainty in the priors is maximized given a performance demand. We apply the method to a two-class medical classification problem. We show that the robust-satisficing decision-rule is more robust to uncertainty in the priors than the optimal Bayesian decision-rule at sub-opti
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Iyer, Akshay, Yichi Zhang, Aditya Prasad, et al. "Data-Centric Mixed-Variable Bayesian Optimization for Materials Design." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98222.

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Abstract Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal
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Reports on the topic "Bayesian Optimal Design"

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Catanach, Thomas, and Kevin Monogue. Analysis and Optimization of Seismo-Acoustic Monitoring Networks with Bayesian Optimal Experimental Design. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1815356.

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