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Journal articles on the topic 'Bayesian Optimal Design'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Hefang, Lin, Raymond H. Myers, and Ye Keying. "Bayesian two-stage optimal design for mixture models." Journal of Statistical Computation and Simulation 66, no. 3 (2000): 209–31. http://dx.doi.org/10.1080/00949650008812023.

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12

Talebi, H., and D. Poursina. "An Efficient Bayesian Optimal Design for Logistic Model." Journal of Statistical Research of Iran 10, no. 2 (2014): 181–96. http://dx.doi.org/10.18869/acadpub.jsri.10.2.181.

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13

Sebastiani, P., and H. P. Wynn. "Maximum entropy sampling and optimal Bayesian experimental design." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62, no. 1 (2000): 145–57. http://dx.doi.org/10.1111/1467-9868.00225.

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14

Kang, Lulu, and V. Roshan Joseph. "Bayesian Optimal Single Arrays for Robust Parameter Design." Technometrics 51, no. 3 (2009): 250–61. http://dx.doi.org/10.1198/tech.2009.08057.

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15

Müller, Peter, Bruno Sansó, and Maria De Iorio. "Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation." Journal of the American Statistical Association 99, no. 467 (2004): 788–98. http://dx.doi.org/10.1198/016214504000001123.

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16

Lin, Ruitao. "Bayesian optimal interval design with multiple toxicity constraints." Biometrics 74, no. 4 (2018): 1320–30. http://dx.doi.org/10.1111/biom.12912.

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17

Li, Jie, and Haoda Fu. "Bayesian Adaptive D-Optimal Design with Delayed Responses." Journal of Biopharmaceutical Statistics 23, no. 3 (2013): 559–68. http://dx.doi.org/10.1080/10543406.2012.755996.

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18

Jones, Matthew, Michael Goldstein, Philip Jonathan, and David Randell. "Bayes linear analysis for Bayesian optimal experimental design." Journal of Statistical Planning and Inference 171 (April 2016): 115–29. http://dx.doi.org/10.1016/j.jspi.2015.10.011.

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19

Tulsyan, Aditya, J. Fraser Forbes, and Biao Huang. "Designing priors for robust Bayesian optimal experimental design." Journal of Process Control 22, no. 2 (2012): 450–62. http://dx.doi.org/10.1016/j.jprocont.2011.12.004.

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20

Felsenstein, K. "Optimal Bayesian design for discrimination among rival models." Computational Statistics & Data Analysis 14, no. 4 (1992): 427–36. http://dx.doi.org/10.1016/0167-9473(92)90058-n.

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21

Tarakanov, Alexadner, and Ahmed H. Elsheikh. "Optimal Bayesian experimental design for subsurface flow problems." Computer Methods in Applied Mechanics and Engineering 370 (October 2020): 113208. http://dx.doi.org/10.1016/j.cma.2020.113208.

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22

Chaloner, Kathryn, and Kinley Larntz. "Optimal Bayesian design applied to logistic regression experiments." Journal of Statistical Planning and Inference 21, no. 2 (1989): 191–208. http://dx.doi.org/10.1016/0378-3758(89)90004-9.

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23

Chaloner, Kathryn. "Correction: Optimal Bayesian Experimental Design for Linear Models." Annals of Statistics 13, no. 2 (1985): 836. http://dx.doi.org/10.1214/aos/1176349564.

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24

Ding, Meichun, Gary L. Rosner, and Peter Müller. "Bayesian Optimal Design for Phase II Screening Trials." Biometrics 64, no. 3 (2007): 886–94. http://dx.doi.org/10.1111/j.1541-0420.2007.00951.x.

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25

van der Linden, Wim J., and Hao Ren. "Optimal Bayesian Adaptive Design for Test-Item Calibration." Psychometrika 80, no. 2 (2014): 263–88. http://dx.doi.org/10.1007/s11336-013-9391-8.

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26

Liu, Xiao, and Loon-Ching Tang. "A Bayesian optimal design for accelerated degradation tests." Quality and Reliability Engineering International 26, no. 8 (2010): 863–75. http://dx.doi.org/10.1002/qre.1151.

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27

Kaluarachchi, Jagath J., and A. H. Wijedasa. "Optimal soil venting design using Bayesian decision analysis." Journal of Hydrology 163, no. 3-4 (1994): 325–46. http://dx.doi.org/10.1016/0022-1694(94)90147-3.

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28

Lee, Jihwan, and Yoo S. Hong. "Design freeze sequencing using Bayesian network framework." Industrial Management & Data Systems 115, no. 7 (2015): 1204–24. http://dx.doi.org/10.1108/imds-03-2015-0095.

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Purpose – Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk. Design/methodology/approach – A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component,

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29

Kamenica, Emir. "Bayesian Persuasion and Information Design." Annual Review of Economics 11, no. 1 (2019): 249–72. http://dx.doi.org/10.1146/annurev-economics-080218-025739.

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A school may improve its students’ job outcomes if it issues only coarse grades. Google can reduce congestion on roads by giving drivers noisy information about the state of traffic. A social planner might raise everyone's welfare by providing only partial information about solvency of banks. All of this can happen even when everyone is fully rational and understands the data-generating process. Each of these examples raises questions of what is the (socially or privately) optimal information that should be revealed. In this article, I review the literature that answers such questions.

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30

Xu, Zhihang, and Qifeng Liao. "Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design." Entropy 22, no. 2 (2020): 258. http://dx.doi.org/10.3390/e22020258.

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Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides

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31

Wizsa, Uqwatul Alma. "BAYESIAN APPROACH TO THE D-OPTIMAL FOR MIXTURE EXPERIMENTAL DESIGN." Jurnal Riset dan Aplikasi Matematika (JRAM) 3, no. 2 (2019): 109. http://dx.doi.org/10.26740/jram.v3n2.p109-115.

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A mixture experiment is a special case of response surface methodology in which the value of the components are proportions. In case there are constraints on the proportions, the experimental region can be not a simplex. The classical designs such as a simplex-lattice design or a simplex-centroid design, in some cases, cannot fit to the problem. In this case, optimal design come up as a solution. A D-optimal design is seeking a design in which minimizing the covariance of the model parameter. Some model parameters are important and some of them are less important. As the priority of the parame

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32

Sujan, Hasan Mahmud, and M. Iftakhar Alam. "D-Optimal Designs for dose Finding in Phase I Clinical Trials." Journal of Statistical Research 55, no. 2 (2022): 313–34. http://dx.doi.org/10.3329/jsr.v55i2.58808.

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Determining the maximum tolerated dose (MTD) is the main challenge of phase I clinical trials. There are many methods in the literature to determine the MTD. The D-optimal design can also be used to find the MTD. The D-optimal design depends on the Fisher information matrix (FIM), and it minimizes the generalized variance of the parameter estimates. However, the D-optimal design is yet to receive much attention from clinicians. Since a dose-response model is usually non-linear, the FIM depends on the unknown model parameters. To optimize the FIM through the D-criterion, values need to be assum

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33

Alhorn, Kira, Holger Dette, and Kirsten Schorning. "Optimal Designs for Model Averaging in non-nested Models." Sankhya A 83, no. 2 (2021): 745–78. http://dx.doi.org/10.1007/s13171-020-00238-9.

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AbstractIn this paper we construct optimal designs for frequentist model averaging estimation. We derive the asymptotic distribution of the model averaging estimate with fixed weights in the case where the competing models are non-nested. A Bayesian optimal design minimizes an expectation of the asymptotic mean squared error of the model averaging estimate calculated with respect to a suitable prior distribution. We derive a necessary condition for the optimality of a given design with respect to this new criterion. We demonstrate that Bayesian optimal designs can improve the accuracy of model

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34

Kudo, Koji, Keita Morimoto, Akito Iguchi, Yasuhide Tsuji, and Tatsuya Kashiwa. "Optimal design of dielectric flat lens utilizing Bayesian optimization." Microwave and Optical Technology Letters 63, no. 7 (2021): 1978–83. http://dx.doi.org/10.1002/mop.32837.

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35

Hwang, Ha-Eun, Yoon-Sang Cho, Seok-Cheol Hwang, and Seoung-Bum Kim. "Optimal Tire Design Using Machine Learning and Bayesian Optimization." Journal of the Korean Institute of Industrial Engineers 48, no. 4 (2022): 433–40. http://dx.doi.org/10.7232/jkiie.2022.48.4.433.

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36

Terejanu, G., C. M. Bryant, and K. Miki. "Bayesian optimal experimental design for the Shock-tube experiment." Journal of Physics: Conference Series 410 (February 8, 2013): 012040. http://dx.doi.org/10.1088/1742-6596/410/1/012040.

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37

Zhou, Xiaojie, Lawrence Joseph, David B. Wolfson, and Patrick Bélisle. "A Bayesian A ‐Optimal and Model Robust Design Criterion." Biometrics 59, no. 4 (2003): 1082–88. http://dx.doi.org/10.1111/j.0006-341x.2003.00124.x.

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38

Chaloner, Kathryn. "A note on optimal Bayesian design for nonlinear problems." Journal of Statistical Planning and Inference 37, no. 2 (1993): 229–35. http://dx.doi.org/10.1016/0378-3758(93)90091-j.

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39

Yue, Rong-Xian, Hong Qin, and Kashinath Chatterjee. "Optimal U-type design for Bayesian nonparametric multiresponse prediction." Journal of Statistical Planning and Inference 141, no. 7 (2011): 2472–79. http://dx.doi.org/10.1016/j.jspi.2011.02.010.

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40

Li, Xiaoyang, Yuqing Hu, Fuqiang Sun, and Rui Kang. "A Bayesian Optimal Design for Sequential Accelerated Degradation Testing." Entropy 19, no. 7 (2017): 325. http://dx.doi.org/10.3390/e19070325.

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41

Li, Xiaoyang, Mohammad Rezvanizaniani, Zhengzheng Ge, Mohamed Abuali, and Jay Lee. "Bayesian optimal design of step stress accelerated degradation testing." Journal of Systems Engineering and Electronics 26, no. 3 (2015): 502–13. http://dx.doi.org/10.1109/jsee.2015.00058.

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42

Chawla, Shuchi, David Malec, and Balasubramanian Sivan. "The power of randomness in Bayesian optimal mechanism design." Games and Economic Behavior 91 (May 2015): 297–317. http://dx.doi.org/10.1016/j.geb.2012.08.010.

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43

Huan, Xun, and Youssef M. Marzouk. "Simulation-based optimal Bayesian experimental design for nonlinear systems." Journal of Computational Physics 232, no. 1 (2013): 288–317. http://dx.doi.org/10.1016/j.jcp.2012.08.013.

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44

Long, Quan, Mohammad Motamed, and Raúl Tempone. "Fast Bayesian optimal experimental design for seismic source inversion." Computer Methods in Applied Mechanics and Engineering 291 (July 2015): 123–45. http://dx.doi.org/10.1016/j.cma.2015.03.021.

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45

Palmer, J. Lynn, and Peter Müller. "Bayesian optimal design in population models for haematologic data." Statistics in Medicine 17, no. 14 (1998): 1613–22. http://dx.doi.org/10.1002/(sici)1097-0258(19980730)17:14<1613::aid-sim867>3.0.co;2-c.

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46

Bania, Piotr. "Bayesian Input Design for Linear Dynamical Model Discrimination." Entropy 21, no. 4 (2019): 351. http://dx.doi.org/10.3390/e21040351.

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A Bayesian design of the input signal for linear dynamical model discrimination has been proposed. The discrimination task is formulated as an estimation problem, where the estimated parameter indexes particular models. As the mutual information between the parameter and model output is difficult to calculate, its lower bound has been used as a utility function. The lower bound is then maximized under the signal energy constraint. Selection between two models and the small energy limit are analyzed first. The solution of these tasks is given by the eigenvector of a certain Hermitian matrix. Ne

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47

Arieli, Itai, Yakov Babichenko, Rann Smorodinsky, and Takuro Yamashita. "Optimal persuasion via bi‐pooling." Theoretical Economics 18, no. 1 (2023): 15–36. http://dx.doi.org/10.3982/te4663.

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Mean‐preserving contractions are critical for studying Bayesian models of information design. We introduce the class of bi‐pooling policies, and the class of bi‐pooling distributions as their induced distributions over posteriors. We show that every extreme point in the set of all mean‐preserving contractions of any given prior over an interval takes the form of a bi‐pooling distribution. By implication, every Bayesian persuasion problem with an interval state space admits an optimal bi‐pooling distribution as a solution, and conversely, for every bi‐pooling distribution, there is a Bayesian p

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48

Abdelbasit, K. M. "Experimental Design for Nonlinear Problems." Sultan Qaboos University Journal for Science [SQUJS] 12, no. 2 (2007): 211. http://dx.doi.org/10.24200/squjs.vol12iss2pp211-220.

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Experimental designs for nonlinear problems have to a large extent relied on optimality criteria originally proposed for linear models. Optimal designs obtained for nonlinear models are functions of the unknown model parameters. They cannot, therefore, be directly implemented without some knowledge of the very parameters whose estimation is sought. The natural way is to adopt a sequential or Bayesian approach. Another is to utilize available estimates or guesses. In this article we provide a brief historical account of the subject, discuss optimality criteria commonly used for nonlinear models

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49

Lomeli, Luis Martinez, Abdon Iniguez, Prasanthi Tata, et al. "Optimal experimental design for mathematical models of haematopoiesis." Journal of The Royal Society Interface 18, no. 174 (2021): 20200729. http://dx.doi.org/10.1098/rsif.2020.0729.

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The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framew

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50

Lauer, S., J. Timmer, D. van Calker, D. Maier, and J. Honerkamp. "Optimal weighted bayesian design applied to dose-response-curve analysis." Communications in Statistics - Theory and Methods 26, no. 12 (1997): 2879–903. http://dx.doi.org/10.1080/03610929708832084.

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