Modeling individual differences using Dirichlet processes
暂无分享,去创建一个
[1] Tony O’Hagan. Bayes factors , 2006 .
[2] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[3] George Karabatsos,et al. Bayesian nonparametric model selection and model testing , 2006 .
[4] Thomas L. Griffiths,et al. Infinite latent feature models and the Indian buffet process , 2005, NIPS.
[5] M. Lee,et al. Modeling individual differences in cognition , 2005, Psychonomic bulletin & review.
[6] Mark A. Pitt,et al. Advances in Minimum Description Length: Theory and Applications , 2005 .
[7] M. Lee. 2 Minimum Description Length and Psychological Clustering Models , 2005 .
[8] Jorma Rissanen,et al. An MDL Framework for Data Clustering , 2005 .
[9] Predicting true patterns of cognitive performance from noisy data , 2004, Psychonomic bulletin & review.
[10] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[11] Padhraic Smyth,et al. Model-Based Clustering and Visualization of Navigation Patterns on a Web Site , 2003, Data Mining and Knowledge Discovery.
[12] Kristin A Duncan,et al. Case and covariate influence: implications for model assessment , 2004 .
[13] K. Dieussaert,et al. Proceedings of the 26th annual conference of the cognitive science society , 2004 .
[14] Michael D. Lee,et al. Modeling Individual Differences in Category Learning , 2004 .
[15] Thomas L. Griffiths,et al. Hierarchical Topic Models and the Nested Chinese Restaurant Process , 2003, NIPS.
[16] Ata Kabán,et al. Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles , 2003, NIPS.
[17] Jeffrey N. Rouder,et al. A hierarchical bayesian statistical framework for response time distributions , 2003 .
[18] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[19] Joshua B. Tenenbaum,et al. Inferring causal networks from observations and interventions , 2003, Cogn. Sci..
[20] M. Tribus,et al. Probability theory: the logic of science , 2003 .
[21] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[22] Radford M. Neal,et al. Density Modeling and Clustering Using Dirichlet Diffusion Trees , 2003 .
[23] H. Ishwaran,et al. Exact and approximate sum representations for the Dirichlet process , 2002 .
[24] M. Peruggia,et al. Was it a car or a cat I saw? An Analysis of Response Times for Word Recognition , 2002 .
[25] B. Junker,et al. Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory , 2001 .
[26] J. Tenenbaum,et al. Generalization, similarity, and Bayesian inference. , 2001, The Behavioral and brain sciences.
[27] P. Green,et al. Modelling Heterogeneity With and Without the Dirichlet Process , 2001 .
[28] W. Michael Conklin,et al. Monte Carlo Methods in Bayesian Computation , 2001, Technometrics.
[29] Paul De Boeck,et al. Multidimensional Componential Item Response Theory Models for Polytomous Items , 2001 .
[30] Lancelot F. James,et al. Gibbs Sampling Methods for Stick-Breaking Priors , 2001 .
[31] M. Lee. Determining the Dimensionality of Multidimensional Scaling Representations for Cognitive Modeling. , 2001, Journal of mathematical psychology.
[32] José M Bernardo and Adrian F M Smith,et al. BAYESIAN THEORY , 2008 .
[33] I. J. Myung,et al. Toward an explanation of the power law artifact: Insights from response surface analysis , 2000, Memory & cognition.
[34] Radford M. Neal. Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .
[35] Wasserman,et al. Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.
[36] W. Godwin. Article in Press , 2000 .
[37] L. Shapley,et al. Statistics, probability, and game theory : papers in honor of David Blackwell , 1999 .
[38] Carl E. Rasmussen,et al. The Infinite Gaussian Mixture Model , 1999, NIPS.
[39] N. Chater,et al. Rational models of cognition , 1998 .
[40] Ling Qin,et al. Nonparametric Bayesian models for item response data , 1998 .
[41] J. Wixted,et al. Genuine power curves in forgetting: A quantitative analysis of individual subject forgetting functions , 1997, Memory & cognition.
[42] Radford M. Neal,et al. Bayesian Learning for Neural Networks (Lecture Notes in Statistical Vol. 118) , 1997 .
[43] T. Landauer,et al. A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .
[44] J. Pitman,et al. The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator , 1997 .
[45] I. J. Myung,et al. Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .
[46] L. Wasserman,et al. The Selection of Prior Distributions by Formal Rules , 1996 .
[47] Peter Green,et al. Markov chain Monte Carlo in Practice , 1996 .
[48] J. Pitman. Some developments of the Blackwell-MacQueen urn scheme , 1996 .
[49] M. Escobar,et al. Bayesian Density Estimation and Inference Using Mixtures , 1995 .
[50] S C McKinley,et al. Investigations of exemplar and decision bound models in large, ill-defined category structures. , 1995, Journal of experimental psychology. Human perception and performance.
[51] Radford M. Neal. Bayesian learning for neural networks , 1995 .
[52] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[53] Bradley P. Carlin,et al. Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .
[54] Wray L. Buntine. Operations for Learning with Graphical Models , 1994, J. Artif. Intell. Res..
[55] Gregory Ashby,et al. On the Dangers of Averaging Across Subjects When Using Multidimensional Scaling or the Similarity-Choice Model , 1994 .
[56] John R. Anderson,et al. The Adaptive Nature of Human Categorization. , 1991 .
[57] J. Sethuraman. A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .
[58] John R. Anderson. The Adaptive Character of Thought , 1990 .
[59] B. M. Hill,et al. Theory of Probability , 1990 .
[60] D. Freedman,et al. On the consistency of Bayes estimates , 1986 .
[61] R. Nosofsky. Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.
[62] D. Aldous. Exchangeability and related topics , 1985 .
[63] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[64] Albert Y. Lo,et al. On a Class of Bayesian Nonparametric Estimates: I. Density Estimates , 1984 .
[65] F. Lord. Applications of Item Response Theory To Practical Testing Problems , 1980 .
[66] Claudio Rebbi,et al. Monte Carlo Study of Abelian Lattice Gauge Theories , 1979 .
[67] C. Antoniak. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .
[68] T. Ferguson. Prior Distributions on Spaces of Probability Measures , 1974 .
[69] R. M. Korwar,et al. Contributions to the Theory of Dirichlet Processes , 1973 .
[70] D. Blackwell,et al. Ferguson Distributions Via Polya Urn Schemes , 1973 .
[71] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[72] D. Blackwell. Discreteness of Ferguson Selections , 1973 .
[73] D. Lindley,et al. Bayes Estimates for the Linear Model , 1972 .
[74] M. Degroot. Optimal Statistical Decisions , 1970 .
[75] J. McCloskey,et al. A model for the distribution of individuals by species in an environment , 1965 .
[76] David M. Miller,et al. Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .
[77] C. Kraft. A class of distribution function processes which have derivatives , 1964, Journal of Applied Probability.
[78] D. Freedman. On the Asymptotic Behavior of Bayes' Estimates in the Discrete Case , 1963 .
[79] Ward Edwards,et al. Bayesian statistical inference for psychological research. , 1963 .
[80] Estes Wk. The problem of inference from curves based on group data. , 1956 .
[81] W. Estes. The problem of inference from curves based on group data. , 1956, Psychological bulletin.
[82] L. M. M.-T.. Theory of Probability , 1929, Nature.