A Tutorial on Adaptive Design Optimization.
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Jay I. Myung | Mark A Pitt | Jay I Myung | Daniel R. Cavagnaro | Daniel R Cavagnaro | M. Pitt | M. Pitt
[1] David J. Weiss,et al. APPLICATION OF COMPUTERIZED ADAPTIVE TESTING TO EDUCATIONAL PROBLEMS , 1984 .
[2] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[3] K. Chaloner,et al. Bayesian Experimental Design: A Review , 1995 .
[4] Christian P. Robert,et al. Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.
[5] R. Hambleton,et al. Fundamentals of Item Response Theory , 1991 .
[6] Jonathan D. Nelson,et al. Experience Matters , 2010, Psychological science.
[7] Peter Grünwald,et al. A tutorial introduction to the minimum description length principle , 2004, ArXiv.
[8] D. Berry. Bayesian clinical trials , 2006, Nature Reviews Drug Discovery.
[9] Jay I. Myung,et al. Optimal experimental design for model discrimination. , 2009, Psychological review.
[10] Warren B. Powell,et al. Approximate Dynamic Programming - Solving the Curses of Dimensionality , 2007 .
[11] Ulrich W. Thonemann,et al. Optimizing simulated annealing schedules with genetic programming , 1996 .
[12] J. Timmer,et al. Systems biology: experimental design , 2009, The FEBS journal.
[13] Cornelis A.W. Glas,et al. Computerized adaptive testing : theory and practice , 2000 .
[14] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.
[15] bak gwansu,et al. An Adaptive Approach to , 2006 .
[16] T. Loredo. Bayesian Adaptive Exploration , 2004, astro-ph/0409386.
[17] L. Reder,et al. A mechanistic account of the mirror effect for word frequency: a computational model of remember-know judgments in a continuous recognition paradigm. , 2000, Journal of experimental psychology. Learning, memory, and cognition.
[18] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[19] Jay I. Myung,et al. Model discrimination through adaptive experimentation , 2011, Psychonomic bulletin & review.
[20] Daniel R. Cavagnaro,et al. Discriminating among probability weighting functions using adaptive design optimization , 2013, Journal of risk and uncertainty.
[21] Mark A. Pitt,et al. Advances in Minimum Description Length: Theory and Applications , 2005 .
[22] L. Wasserman,et al. Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .
[23] J. Wixted,et al. On the Form of Forgetting , 1991 .
[24] Sean M. Polyn,et al. A context maintenance and retrieval model of organizational processes in free recall. , 2009, Psychological review.
[25] D. Rubin,et al. One Hundred Years of Forgetting : A Quantitative Description of Retention , 1996 .
[26] P. Müller,et al. Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation , 2004 .
[27] Luis A. Lesmes,et al. Bayesian adaptive estimation of threshold versus contrast external noise functions: The quick TvC method , 2006, Vision Research.
[28] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[29] T. Louis,et al. Bayes and Empirical Bayes Methods for Data Analysis. , 1997 .
[30] Mark A. Pitt,et al. Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach , 2013, Manag. Sci..
[31] S. Lewandowsky,et al. Forgetting in immediate serial recall: decay, temporal distinctiveness, or interference? , 2008, Psychological review.
[32] David R. Anderson,et al. Model selection and multimodel inference : a practical information-theoretic approach , 2003 .
[33] Bradley P. Carlin,et al. BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..
[34] Peter Grünwald,et al. Accumulative prediction error and the selection of time series models , 2006 .
[35] Zhong-Lin Lu,et al. Bayesian adaptive estimation of the contrast sensitivity function: the quick CSF method. , 2010, Journal of vision.
[36] Jonathan D. Nelson. Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain. , 2005, Psychological review.
[37] C. Tyler,et al. Bayesian adaptive estimation of psychometric slope and threshold , 1999, Vision Research.
[38] L. Wasserman,et al. The Selection of Prior Distributions by Formal Rules , 1996 .
[39] Tuomas J. Lukka,et al. Bayesian adaptive estimation: The next dimension , 2006 .
[40] D. D. Bickerstaff,et al. Computerized Adaptive Testing , 1989 .
[41] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[42] William J. Hill,et al. Discrimination Among Mechanistic Models , 1967 .
[43] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[44] Anthony C. Atkinson,et al. Optimum Experimental Designs , 1992 .
[45] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[46] Petros Dellaportas,et al. An Introduction to MCMC , 2003 .
[47] Christian P. Robert,et al. Bayesian-Optimal Design via Interacting Particle Systems , 2006 .
[48] A. Rukhin. Bayes and Empirical Bayes Methods for Data Analysis , 1997 .
[49] Mark M. Tanaka,et al. Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.
[50] Christian P. Robert,et al. The Bayesian choice : from decision-theoretic foundations to computational implementation , 2007 .
[51] David A. Cohn,et al. Improving generalization with active learning , 1994, Machine Learning.
[52] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[53] L. M. M.-T.. Theory of Probability , 1929, Nature.
[54] Brandon M. Turner,et al. A tutorial on approximate Bayesian computation , 2012 .
[55] E. Vul,et al. Functional adaptive sequential testing. , 2010, Seeing and perceiving.
[56] A. Atkinson,et al. Optimal design : Experiments for discriminating between several models , 1975 .
[57] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[58] A. Pettitt,et al. Introduction to MCMC , 2012 .
[59] Mark A. Pitt,et al. Adaptive Design Optimization in Experiments with People , 2009, NIPS.
[60] J. Kruschke. Doing Bayesian Data Analysis: A Tutorial with R and BUGS , 2010 .
[61] Alan B. Cobo-Lewis,et al. An adaptive psychophysical method for subject classification , 1997, Perception & psychophysics.
[62] I. J. Myung,et al. Toward a method of selecting among computational models of cognition. , 2002, Psychological review.
[63] Mark A. Pitt,et al. Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science , 2010, Neural Computation.
[64] D. Lindley. On a Measure of the Information Provided by an Experiment , 1956 .
[65] Peter F Thall,et al. Bayesian adaptive model selection for optimizing group sequential clinical trials , 2008, Statistics in medicine.
[66] Robert J. Butera,et al. Sequential Optimal Design of Neurophysiology Experiments , 2009, Neural Computation.
[67] P. Laycock,et al. Optimum Experimental Designs , 1995 .
[68] P. Grünwald,et al. Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC–BIC dilemma , 2012 .
[69] Edward A. Vessel,et al. Beauty and the beholder: highly individual taste for abstract, but not real-world images. , 2010, Journal of vision.
[70] Jay I. Myung,et al. Optimal Inference and Feedback for Representational Change , 2010 .
[71] P. Müller. Simulation Based Optimal Design , 2005 .