A tutorial on adaptive design optimization

Journal of Mathematical Psychology

Volumn 57, Issue 3-4, 2013, Pages 53-67

  Myung, Jay I ^a   Cavagnaro, Daniel R ^b   Pitt, Mark A ^a  

^a OHIO STATE UNIVERSITY   (United States)

^b CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

Bayesian adaptive estimation; Cognitive modeling; Mutual information; Optimal experimental design; Sequential Monte Carlo

Indexed keywords

EID: 84882840400     PISSN: 00222496     EISSN: 10960880     Source Type: Journal    
DOI: 10.1016/j.jmp.2013.05.005     Document Type: Review

References (64)

1. Akaike H. Information theory and an extension of the maximum likelihood principle. Proceedings of the Second International Symposium on Information Theory 1973, 267-281. Akademiai Kiado, Budapest. B.N. Petrov, F. Caski (Eds.).
2. Amzal B., Bois F., Parent E., Robert C. Bayesian-optimal design via interacting particle systems. Journal of the American Statistical Association 2006, 101(474):773-785.
3. Andrieu C., DeFreitas N., Doucet A., Jornan M.J. An introduction to MCMC for machine learning. Machine Learning 2003, 50:5-43.
4. Atkinson A., Donev A. Optimum Experimental Designs 1992, Oxford University Press.
5. Atkinson A., Federov V. Optimal design: experiments for discriminating between several models. Biometrika 1975, 62(2):289.
6. Beaumont M.A., Cornuet J.-M., Marin J.-M., Robert C.P. Adaptive approximate Bayesian computation. Biometrika 2009, 52:1-8.
7. Bellman R.E. Dynamic Programming (reprint edition) 2003, Dover Publications, Mineola, NY.
8. Berry D.A. Bayesian clinical trials. Nature Reviews 2006, 5:27-36.
9. Bölte A., Thonemann U.W. Optimizing simulated annealing schedule with genertic programming. European Journal of Operational Research 1996, 92:402-416.
10. Box G., Hill W. Discrimination among mechanistic models. Technometrics 1967, 9:57-71.
11. Burnham K.P., Anderson D.R. Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach 2010, Springer, New York, NY. 2nd ed.
12. Carlin H.P., Louis T.A. Bayes and Empirical Bayes Methods for Data Analysis 2000, Chapman & Hall. 2nd ed.
13. Cavagnaro D., Gonzalez R., Myung J., Pitt M. Optimal decision stimuli for risky choice experiments: an adaptive approach. Management Science 2013, 59(2):358-375.
14. Cavagnaro D.R., Myung J.I., Pitt M.A., Kujala J.V. Adaptive design optimization: a mutual information based approach to model discrimination in cognitive science. Neural Computation 2010, 22(4):887-905.
15. Cavagnaro D., Pitt M., Gonzalez R., Myung J. Discriminating among probability weighting functions using adaptive design optimization. Journal of Risk and Uncertainty 2013, (in press).
16. Cavagnaro D., Pitt M., Myung J. Model discrimination through adaptive experimentation. Psychonomic Bulletin & Review 2011, 18(1):204-210.
17. Cavagnaro D.R., Pitt M.A., Myung J.I. Adaptive design optimization in experiments with people. Advances in Neural Information Processing Systems 2009, 22:234-242.
18. Chaloner K., Verdinelli I. Bayesian experimental design: a review. Statistical Science 1995, 10(3):273-304.
19. Cobo-Lewis A.B. An adaptive psychophysical method for subject classification. Perception & Psychophysics 1997, 59:989-1003.
20. Cohn D., Atlas L., Ladner R. Improving generalization with active learning. Machine Learning 1994, 15(2):201-221.
21. Cohn D., Ghahramani Z., Jordan M. Active learning with statistical models. Journal of Artificial Intelligence Research 1996, 4:129-145.
22. Cover T., Thomas J. Elements of Information Theory 1991, John Wiley & Sons, Inc.
23. Doucet A., de Freitas N., Gordon N. Sequential Monte Carlo Methods in Practice 2001, Springer.
24. Gelman A., Carlin J., Stern H., Rubin D. Bayesian Data Analysis 2004, Chapman & Hall.
25. Gill J. Bayesian Methods: A Social and Behavioral Sciences 2007, Chapman and Hall/CRC, New York, NY. 2nd ed.
26. Grünwald P.D. A tutorial introduction to the minimum description length principle. Advances in Minimum Description Length: Theory and Applications 2005, The M.I.T. Press. P. Grünwald, I.J. Myung, M.A. Pitt (Eds.).
27. Hambleton R.K., Swaminathan H., Rogers H.J. Fundamentals of Item Response Theory 1991, Sage Publications, Newbury Park, CA.
28. Jeffreys H. Theory of Probability 1961, Oxford University Press, Oxford, UK.
29. Kass R.E., Raftery A.E. Bayes factors. Journal of the American Statistical Association 1995, 90:773-795.
30. Kass R.E., Wasserman L. The selection of prior distributions by formal rules. Journal of the American Statistical Association 1996, 91(435):1343-1370.
31. Kirkpatrick S., Gelatt C., Vecchi M. Optimization by simulated annealing. Science 1983, 220:671-680.
32. Kontsevich L.L., Tyler C.W. Bayesian daptive estimation of psychometric slope and threshold. Vision Research 1999, 39:2729-2737.
33. Kreutz C., Timmer J. Systems biology: experimental design. FEBS Journal 2009, 276:923-942.
34. Kruschke J.K. Doing Bayesian Data Analysis: A Tutorial with R and BUGS 2010, Academic Press, New York, NY.
35. Kujala J., Lukka T. Bayesian adaptive estimation: the next dimension. Journal of Mathematical Psychology 2006, 50(4):369-389.
36. Lesmes L., Jeon S.-T., Lu Z.-L., Dosher B. Bayesian adaptive estimation of threshold versus contrast external noise functions: the quick TvC method. Vision Research 2006, 46:3160-3176.
37. Lesmes L., Lu Z.-L., Baek J., Dosher B. Bayesian adaptive estimation of the contrast sensitivity function: the quick SCF method. Journal of Vision 2010, 10:1-21.
38. Lewi J., Butera R., Paninski L. Sequential optimal design of neurophysiology experiments. Neural Computation 2009, 21:619-687.
39. Lindley D. On a measure of the information provided by an experiment. Annals of Mathematical Statistics 1956, 27(4):986-1005.
40. Loredo T.J. Bayesian adaptive exploration. Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering 2004, Vol. 707:330-346. American Institute of Physics. G.J. Erickson, Y. Zhai (Eds.).
41. Müller P. Simulation-based optimal design. Bayesian Statistics 1999, Vol. 6:459-474. Oxford University Press, Oxford, UK. J.O. Berger, A.P. Dawid, A.F.M. Smith (Eds.).
42. Müller P., Sanso B., De Iorio M. Optimal Bayesian design by inhomogeneous Markov chain simulation. Journal of the American Statistical Association 2004, 99(467):788-798.
43. Myung J.I., Pitt M.A. Optimal experimental design for model discrimination. Psychological Review 2009, 58:499-518.
44. Nelson J. Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain. Psychological Review 2005, 112(4):979-999.
45. Nelson J., McKenzie C.R.M., Cottrell G.W., Sejnowski T.J. Experience matters: information acquisition optimizes probability gain. Psychological Science 2011, 21(7):960-969.
46. Oberauer K., Lewandowsky S. Forgetting in immediate serial recall: decay, temporal distinctiveness, or interference?. Psychological Review 2008, 115(3):544-576.
47. Pitt M.A., Myung I.J., Zhang S. Toward a method of selecting among computational models of cognition. Psychological Review 2002, 190(3):472-491.
48. Polyn S.M., Norman K.A., Kahana M.J. A context maintenance and retrieval model of organizational processes in free recall. Psychological Review 2009, 116:129-156.
49. Powell W.B. Approximate Dynamic Programming: Solving the Curses of Dimensionality 2007, John Wiley & Sons, Hoboken, New Jergey.
50. Reder L.M., Nhouyvanisvong A., Schunn C.D., Avyers M.S., Angstadt P., Hiraki K. A mechanistic account of the mirror effect for word frequency: a computational model of remember-know judgments in a continuous recognition paradigm. Journal of Experimental Psychology: Learning, Memory and Cognition 2000, 26:294-320.
51. Rissanen J. Modeling by shortest data description. Automatica 1978, 14:461-471.
52. Robert C.P. The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation 2001, Springer, New York, NY. 2nd ed.
53. Robert C.P., Casella G. Monte Carlo Statistical Methods 2004, Springer, New York, NY. 2nd ed.
54. Rubin D., Wenzel A. One hundred years of forgetting: a quantitative description of retention. Psychological Review 1996, 103(4):734-760.
55. Sutton R.S., Barto A.G. Reinforcement Learning: An Introduction 1998, MIT Press, Cambridge, Massachusetts.
56. Tang Y., Young C., Myung J.I., Pitt M.A., Opfer J. Optimal inference and feedback for representational change. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society 2010, 2572-2577. Cognitive Science Society, Austin, TX. S. Ohlsson, R. Catrambone (Eds.).
57. Turner B., Van Zandt T. A tutorial on approximate bayesian computation. Journal of Mathematical Psychology 2012, 56(2):69-85.
58. van der Linden W.J., Glas C.A.W. Computerized Adaptive Testing 2000, Kluwer Academic Publishers, Boston, MA.
59. van Erven T., Grünwald P.D., de Rooji S. Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC-BIC dilemma. Journal of the Royal Statistical Society, Series B 2012, 74:361-397.
60. Vul E., Bergsma J., MacLeod I.A. Functional adaptive sequential testing. Seeing and Perceiving 2010, 23:483-513.
61. Wagenmakers E.-J., Grünwald P.D., Steyvers M. Accumulative prediction error and the selection of time series models. Journal of Mathematical Psychology 2006, 50:149-166.
62. Wathen J.K., Thall P.F. Bayesian adaptive model selection for optimizing group sequential clinical trials. Statistics in Medicine 2008, 27:5586-5604.
63. Weiss D.J., Kingsbury G.G. Application of computerized adaptive testing to educational problems. Journal of Educational Measurement 1984, 21:361-375.
64. Wixted J., Ebbesen E. On the form of forgetting. Psychological Science 1991, 2(6):409-415.