Bayes factors: Prior sensitivity and model generalizability
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[1] E. Wagenmakers. A practical solution to the pervasive problems ofp values , 2007, Psychonomic bulletin & review.
[2] P. Grünwald. The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .
[3] C. Robert,et al. Deviance information criteria for missing data models , 2006 .
[4] Kristopher J Preacher,et al. Quantifying Parsimony in Structural Equation Modeling , 2006, Multivariate behavioral research.
[5] J. Berger. The case for objective Bayesian analysis , 2006 .
[6] E. Wagenmakers,et al. A Bayesian Perspective on Hypothesis Testing , 2006, Psychological science.
[7] George Karabatsos,et al. Bayesian nonparametric model selection and model testing , 2006 .
[8] Jay I. Myung,et al. Model selection by Normalized Maximum Likelihood , 2006 .
[9] Peter Grünwald,et al. Accumulative prediction error and the selection of time series models , 2006 .
[10] Jay I. Myung,et al. Global model analysis by parameter space partitioning. , 2019, Psychological review.
[11] Herbert Hoijtink,et al. Inequality constrained analysis of variance: a Bayesian approach. , 2005, Psychological methods.
[12] Jun Lu,et al. An introduction to Bayesian hierarchical models with an application in the theory of signal detection , 2005, Psychonomic bulletin & review.
[13] M. Lee,et al. Modeling individual differences in cognition , 2005, Psychonomic bulletin & review.
[14] M. Lee,et al. Bayesian statistical inference in psychology: comment on Trafimow (2003). , 2005, Psychological review.
[15] Murray Aitkin,et al. Bayesian point null hypothesis testing via the posterior likelihood ratio , 2005, Stat. Comput..
[16] Jay I. Myung,et al. A Bayesian approach to testing decision making axioms , 2005 .
[17] Frank Jäkel,et al. Bayesian inference for psychometric functions. , 2005, Journal of vision.
[18] J-P Fox,et al. Multilevel IRT using dichotomous and polytomous response data. , 2005, The British journal of mathematical and statistical psychology.
[19] P. Congdon. Bayesian predictive model comparison via parallel sampling , 2005, Comput. Stat. Data Anal..
[20] Jeffrey N. Rouder,et al. A hierarchical model for estimating response time distributions , 2005, Psychonomic bulletin & review.
[21] Mark A. Pitt,et al. Advances in Minimum Description Length: Theory and Applications , 2005 .
[22] Andrew Gelman,et al. R2WinBUGS: A Package for Running WinBUGS from R , 2005 .
[23] Mark A. Pitt,et al. Model Evaluation, Testing and Selection , 2005 .
[24] A. Brix. Bayesian Data Analysis, 2nd edn , 2005 .
[25] Michael D. Lee,et al. A Bayesian analysis of retention functions , 2004 .
[26] Jay I. Myung,et al. Assessing the distinguishability of models and the informativeness of data , 2004, Cognitive Psychology.
[27] Karl J. Friston,et al. Comparing dynamic causal models , 2004, NeuroImage.
[28] M. Lee,et al. Evidence accumulation in decision making: Unifying the “take the best” and the “rational” models , 2004, Psychonomic bulletin & review.
[29] E. Wagenmakers,et al. AIC model selection using Akaike weights , 2004, Psychonomic bulletin & review.
[30] Roger Ratcliff,et al. Assessing model mimicry using the parametric bootstrap , 2004 .
[31] John H. Maindonald,et al. This Passionate Study: A Dialogue with Florence Nightingale , 2004 .
[32] Pedro M. Domingos. The Role of Occam's Razor in Knowledge Discovery , 1999, Data Mining and Knowledge Discovery.
[33] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[34] A. Raftery,et al. Discussion: Performance of Bayesian Model Averaging , 2003 .
[35] Jörg Rieskamp,et al. How do people learn to allocate resources? Comparing two learning theories. , 2003, Journal of experimental psychology. Learning, memory, and cognition.
[36] M. Lee,et al. The roles of the convex hull and the number of potential intersections in performance on visually presented traveling salesperson problems , 2003, Memory & cognition.
[37] A. Gelfand,et al. Inequalities between expected marginal log‐likelihoods, with implications for likelihood‐based model complexity and comparison measures , 2003 .
[38] W. Batchelder,et al. Markov chain estimation for test theory without an answer key , 2003 .
[39] M. Tribus,et al. Probability theory: the logic of science , 2003 .
[40] Woojae Kim,et al. Flexibility versus generalizability in model selection , 2003, Psychonomic bulletin & review.
[41] J. Bernardo,et al. Bayesian Hypothesis Testing: a Reference Approach , 2002 .
[42] I. J. Myung,et al. When a good fit can be bad , 2002, Trends in Cognitive Sciences.
[43] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[44] Hal S. Stern,et al. On the Sensitivity of Bayes Factors to the Prior Distributions , 2002 .
[45] I. J. Myung,et al. Toward a method of selecting among computational models of cognition. , 2002, Psychological review.
[46] Jeff Gill,et al. Bayesian Methods : A Social and Behavioral Sciences Approach , 2002 .
[47] M. Aitkin. Likelihood and Bayesian analysis of mixtures , 2001 .
[48] Bradley P. Carlin,et al. Markov Chain Monte Carlo Methods for Computing Bayes Factors , 2001 .
[49] Jorma Rissanen,et al. Strong optimality of the normalized ML models as universal codes and information in data , 2001, IEEE Trans. Inf. Theory.
[50] M. Lee. Determining the Dimensionality of Multidimensional Scaling Representations for Cognitive Modeling. , 2001, Journal of mathematical psychology.
[51] I. J. Myung,et al. Counting probability distributions: Differential geometry and model selection , 2000, Proc. Natl. Acad. Sci. USA.
[52] I. J. Myung,et al. The Importance of Complexity in Model Selection. , 2000, Journal of mathematical psychology.
[53] J. Busemeyer,et al. Model Comparisons and Model Selections Based on Generalization Criterion Methodology. , 2000, Journal of mathematical psychology.
[54] Golden,et al. Statistical Tests for Comparing Possibly Misspecified and Nonnested Models. , 2000, Journal of mathematical psychology.
[55] Wasserman,et al. Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.
[56] Zucchini,et al. An Introduction to Model Selection. , 2000, Journal of mathematical psychology.
[57] M. Schervish,et al. Bayes Factors: What They are and What They are Not , 1999 .
[58] D. Weakliem. A Critique of the Bayesian Information Criterion for Model Selection , 1999 .
[59] A. Raftery. Bayes Factors and BIC , 1999 .
[60] Donald B. Rubin,et al. Evaluating and Using Statistical Methods in the Social Sciences , 1999 .
[61] Alan E. Gelfand,et al. Model choice: A minimum posterior predictive loss approach , 1998, AISTATS.
[62] T. Wickens. On the form of the retention function : Comment on Rubin and Wenzel (1996) : A quantitative description of retention , 1998 .
[63] Arthur P. Dempster,et al. The direct use of likelihood for significance testing , 1997, Stat. Comput..
[64] Murray Aitkin. The calibration of P-values, posterior Bayes factors and the AIC from the posterior distribution of the likelihood , 1997, Stat. Comput..
[65] J. Wixted,et al. Genuine power curves in forgetting: A quantitative analysis of individual subject forgetting functions , 1997, Memory & cognition.
[66] I. J. Myung,et al. Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .
[67] V. Balasubramanian. Statistical Inference, Occam's Razor, and Statistical Mechanics on the Space of Probability Distributions , 1996, Neural Computation.
[68] Amy Wenzel,et al. One hundred years of forgetting: A quantitative description of retention , 1996 .
[69] L. Wasserman,et al. The Selection of Prior Distributions by Formal Rules , 1996 .
[70] J. Berger,et al. The Intrinsic Bayes Factor for Model Selection and Prediction , 1996 .
[71] John Skilling,et al. Data analysis : a Bayesian tutorial , 1996 .
[72] Xiao-Li Meng,et al. POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .
[73] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[74] S. Chib. Marginal Likelihood from the Gibbs Output , 1995 .
[75] A. Raftery. Bayesian Model Selection in Social Research , 1995 .
[76] Donald B. Rubin,et al. Avoiding Model Selection in Bayesian Social Research , 1995 .
[77] A. O'Hagan,et al. Fractional Bayes factors for model comparison , 1995 .
[78] A. Jacobs,et al. Models of visual word recognition: Sampling the state of the art. , 1994 .
[79] R. Kass. Bayes Factors in Practice , 1993 .
[80] Anne Boomsma,et al. Cross-Validation in Regression and Covariance Structure Analysis , 1992 .
[81] A. Dawid. Fisherian Inference in Likelihood and Prequential Frames of Reference , 1991 .
[82] L. Squire,et al. On the course of forgetting in very long-term memory. , 1989, Journal of experimental psychology. Learning, memory, and cognition.
[83] Seymour Geisser,et al. On Prior Distributions for Binary Trials , 1984 .
[84] A. P. Dawid,et al. Present position and potential developments: some personal views , 1984 .
[85] G. Shafer. Lindley's Paradox , 1982 .
[86] Edward E. Leamer,et al. Information Criteria for Choice of Regression Models: A Comment , 1979 .
[87] Takamitsu Sawa,et al. Information criteria for discriminating among alternative regression models / BEBR No. 455 , 1978 .
[88] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[89] D. Lindley. A STATISTICAL PARADOX , 1957 .