A hierarchical adaptive approach to optimal experimental design

Neural Computation

Volumn 26, Issue 11, 2014, Pages 2465-2492

  Kim, Woojae ^a   Pitt, Mark A ^a   Lu, Zhong Lin ^a   Steyvers, Mark ^b   Myung, Jay I ^a  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BAYES THEOREM; COMPUTER SIMULATION; HUMAN; METHODOLOGY; STATISTICAL ANALYSIS; VISION;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; RESEARCH DESIGN; VISUAL PERCEPTION;

EID: 84931324513     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00654     Document Type: Article

References (55)

1. Amcs, F. Y., Parent, E., & Robert, C. P. (2006). Bayesian-optimal design via interacting particle systems. Journal of the American Statistical Association, 101(474), 773-785.
2. Atkinson, A., & Donev, A. (1992). Optimum experimental designs. New York: Oxford University Press.
3. Bernado, J. M., & Smith, A. F. M. (1994). Bayesian theory. New York:Wiley.
4. Box, G. F. B., & Hill,W. J. (1967). Discrimination among mechanistic models. Technometrics, 9, 57-71.
5. Cavagnaro, D. R., Gonzalez, R., Myung, J. I., & Pitt, M. A. (2013). Optimal decision stimuli for risky choice experiments: An adaptive approach. Management Science, 59(2), 358-375.
6. Cavagnaro, D. R., Myung, J. I., Pitt, M. A., & Kujala, J. V. (2010). Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science. Neural Computation, 22(4), 887-905.
7. Cavagnaro, D. R., Pitt, M. A., Gonzalez, R., & Myung, J. I. (2013). Discriminating among probability weighting functions using adaptive design optimization. Journal of Risk and Uncertainty, 47(3), 255-289.
8. Cavagnaro, D. R., Pitt, M. A., & Myung, J. I. (2009). Adaptive design optimization in experiments with people. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotte (Eds.), Advances in neural information processing systems, 22 (pp. 234- 242). Cambridge, MA: MIT Press.
9. Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273-304.
10. Chernoff, H. (1959). Sequential design of experiments. Annals of Mathematical Statistics, 755-770.
11. Cohn, D., Ghahramani, Z., & Jordan, M. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129-145.
12. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. Hoboken, NJ: Wiley.
13. de Finetti, B. (1974). Theory of probability. New York: Wiley.
14. DiMattina, C., & Zhang, K. (2008). How optimal stimuli for sensory neurons are constrained by network architecture. Neural Computation, 20, 668-708.
15. DiMattina, C., & Zhang, K. (2011). Active data collection for efficient estimation and comparison of nonlinear neural models. Neural Computation, 23, 2242-2288.
16. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC.
17. Good, I. J. (1965). The estimation of probabilities: An essay on modern bayesian methods. Cambridge, MA: MIT Press.
18. Griebel, M., & Holtz, M. (2010). Dimension-wise integration of high-dimensional functions with applications to finance. Journal of Complexity, 26(5), 455-489.
19. Heiss, F., &Winschel, V. (2008). Likelihood approximation by numerical integration on sparse grids. Journal of Econometrics, 144(1), 62-80.
20. Holtz, M. (2011). Sparse grid quadrature in high dimensions with applications in finance and insurance. New York: Springer.
21. Hou, F., Huang, C. B., Lesmes, L., Feng, L. X., Tao, L., Zhou, Y. F., . . . Lu, Z.-L. (2010). qCSF in clinical application: Efficient characterization and classification of contrast sensitivity functions in amblyopia. Investigative Ophthalmology and Visual Science, 51(10), 5365-5377.
22. Jordan, M. I. (Ed.). (1998). Learning in graphical models. Cambridge, MA: MIT Press.
23. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795.
24. Kiefer, J. (1959).Optimum experimental designs. Journal of the Royal Statistical Society. Series B (Methodological), 21(2), 272-319.
25. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT Press.
26. Kujala, J. V., & Lukka, T. J. (2006). Bayesian adaptive estimation: The next dimension. Journal of Mathematical Psychology, 50(4), 369-389.
27. Lee, M. D. (2006). A hierarchical Bayesian model of human decision-making on an optimal stopping problem. Cognitive Science, 30, 1-26.
28. Lesmes, L. A., Jeon, S.-T., Lu, Z.-L., & Dosher, B. A. (2006). Bayesian adaptive estimation of threshold versus contrast external noise functions: The quick TvC method. Vision Research, 46, 3160-3176.
29. Lesmes, L. A., Lu, Z.-L., Baek, J., & Albright, T. D. (2010). Bayesian adaptive estimation of the contrast sensitivity function: The quick CSF method. Journal of Vision, 10, 1-21.
30. Lewi, J., Butera, R., & Paninski, L. (2009). Sequential optimal design of neurophysiology experiments. Neural Computation, 21, 619-687.
31. Lindley, D. V. (1956). On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27(4), 986-1005.
32. MacEachern, S. N. (2000). Dependent Dirichlet processes (Tech. Rep.). Columbus: Ohio State University.
33. Müller, P., Sanso, B., & De Iorio, M. (2004). Optimal Bayesian design by inhomogeneous Markov chain simulation. Journal of the American Statistical Association, 99(467), 788-798.
34. Myung, J. I., Cavagnaro, D. R., & Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of Mathematical Psychology, 57, 53-67.
35. Nelson, J.D., McKenzie, C. R.M., Cottrell, G.W.,&Sejnowski, T. J. (2011). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21(7), 960-969.
36. Paninski, L. (2003). Estimation of entropy and mutual information. Neural Computation, 15, 1191-1253.
37. Paninski, L. (2005).Asymptotic theory of information-theoretic experimental design. Neural Computation, 17, 1480-1507.
38. Park, M., & Pillow, J. W. (2012). Bayesian active learning with localized priors for fast receptive field characterization. In P. Bartlett, F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems, 25 (pp. 2357-2365). Red Hook, NY: Curran.
39. Pflüger, D., Peherstorfer, B., & Bungartz, H.-J. (2010). Spatially adaptive sparse grids for high-dimensional data-driven problems. Journal of Complexity, 26(5), 508-522.
40. Ramirez,A.D., Ahmadian, Y., Schumacher, J., Schneider,D.,Woolley, S. M.,&Paninski, L. (2011). Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain. Journal of Neuroscience, 31(10), 3828-3842.
41. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Cambridge, MA: MIT Press.
42. Robert, C. P., & Casella, G. (2004). Monte Carlo statistical methods (2nd ed.). New York: Springer.
43. Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin and Review, 12, 573-604.
44. Rouder, J. N., Speckman, P. L., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions Psychonomic Bulletin and Review, 12, 195-223.
45. Scott, D.W. (1992). Multivariate density estimation: Theory, practice, and visualization. New York:Wiley.
46. Settles, B. (2010). Active learning literature survey (Tech. Rep.).Madison: University of Wisconsin-Madison.
47. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Workshop on Computational Learning Theory (pp. 287-294). San Mateo, CA: Morgan Kaufman.
48. Sugiyama, M., & Rubens, N. (2008). A batch ensemble approach to active learning with model selection. Neural Networks, 21(9), 1278-1286.
49. Tang, Y., Young, C., Myung, J. I., Pitt, M. A., & Opfer, J. (2010). Optimal inference and feedback for representational change. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (pp. 2572- 2577). Austin, TX: Cognitive Science Society.
50. Teh, Y. W., & Jordan, M. I. (2010). Hierarchical Bayesian nonparametric models with applications. In N. Hjort, C. Holmes, P. Müller, & S. Walker (Eds.), Bayesian nonparametrics. Cambridge: Cambridge University Press.
51. Tong, S., & Koller, D. (2002). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2, 45-66.
52. Tulsyan, A., Forbes, J. F., & Huang, B. (2012). Designing priors for robust Bayesian optimal experimental design. Journal of Process Control, 22(2), 450-462.
53. Watson, A. B., & Ahumada, A. J. (2005). A standard model for foveal detection of spatial contrast. Journal of Vision, 5(9), 717-740.
54. Woolley, S. M., Gill, P. R., & Theunissen, F. E. (2006). Stimulus-dependent auditory tuning results in synchronous population coding of vocalizations in the songbird midbrain. Journal of Neuroscience, 26(9), 2499-2512.
55. Wu, M. C.-K., David, S. V., & Gallant, J. L. (2006). Complete functional characterization of sensory neurons by system identification. Annual Review of Neuroscience, 29, 477-505.