Using bagged posteriors for robust inference and model criticism
暂无分享,去创建一个
[1] Stephen Walker,et al. Nonparametric learning from Bayesian models with randomized objective functions , 2018, NeurIPS.
[2] David M. Blei,et al. Population Predictive Checks , 2019, ArXiv.
[3] C. Aitken,et al. The logic of decision , 2014 .
[4] Ziheng Yang. Empirical evaluation of a prior for Bayesian phylogenetic inference , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.
[5] G. Box. Robustness in the Strategy of Scientific Model Building. , 1979 .
[6] M. Peligrad,et al. ON THE BLOCKWISE BOOTSTRAP FOR EMPIRICAL PROCESSES FOR STATIONARY SEQUENCES , 1998 .
[7] B. Efron. Bootstrap Methods: Another Look at the Jackknife , 1979 .
[8] W. Doolittle,et al. Comparison of Bayesian and maximum likelihood bootstrap measures of phylogenetic reliability. , 2003, Molecular biology and evolution.
[9] David B. Dunson,et al. Robust Bayesian Inference via Coarsening , 2015, Journal of the American Statistical Association.
[10] Jon A. Wellner,et al. Weak Convergence and Empirical Processes: With Applications to Statistics , 1996 .
[11] A. Dawid,et al. Posterior Model Probabilities , 2011 .
[12] Ziheng Yang,et al. Fair-balance paradox, star-tree paradox, and Bayesian phylogenetics. , 2007, Molecular biology and evolution.
[13] Pier Giovanni Bissiri,et al. A general framework for updating belief distributions , 2013, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[14] Ryan Martin,et al. Gibbs posterior inference on value-at-risk , 2018, Scandinavian Actuarial Journal.
[15] Van Der Vaart,et al. The Bernstein-Von-Mises theorem under misspecification , 2012 .
[16] Kai Zhang,et al. Models as Approximations I: Consequences Illustrated with Linear Regression , 2014, Statistical Science.
[17] Joseph Hilbe,et al. Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .
[18] I. Guttman. The Use of the Concept of a Future Observation in Goodness‐Of‐Fit Problems , 1967 .
[19] Peter Buhlmann. Discussion of Big Bayes Stories and BayesBag , 2014, 1405.4977.
[20] N. Hjort,et al. Post-Processing Posterior Predictive p Values , 2006 .
[21] Ulrich K. Müller. RISK OF BAYESIAN INFERENCE IN MISSPECIFIED MODELS, AND THE SANDWICH COVARIANCE MATRIX , 2013 .
[22] M. Newton. Approximate Bayesian-inference With the Weighted Likelihood Bootstrap , 1994 .
[23] Ziheng Yang,et al. Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees , 2018, Proceedings of the National Academy of Sciences.
[24] Jeffrey W. Miller,et al. Robust and Reproducible Model Selection Using Bagged Posteriors , 2020 .
[25] Andreas Buja,et al. Models as Approximations II: A Model-Free Theory of Parametric Regression , 2016, Statistical Science.
[26] B. Rannala,et al. Frequentist properties of Bayesian posterior probabilities of phylogenetic trees under simple and complex substitution models. , 2004, Systematic biology.
[27] James M. Robins,et al. Asymptotic Distribution of P Values in Composite Null Models , 2000 .
[28] R. Berk,et al. Limiting Behavior of Posterior Distributions when the Model is Incorrect , 1966 .
[29] Aki Vehtari,et al. A survey of Bayesian predictive methods for model assessment, selection and comparison , 2012 .
[30] S. Haneuse,et al. On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses , 2009, The American statistician.
[31] O. Kallenberg. Foundations of Modern Probability , 2021, Probability Theory and Stochastic Modelling.
[32] E. Lehmann. Model Specification: The Views of Fisher and Neyman, and Later Developments , 1990 .
[33] Chris Holmes,et al. General Bayesian updating and the loss-likelihood bootstrap , 2017, Biometrika.
[34] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[35] Emily C. Moriarty,et al. The importance of proper model assumption in bayesian phylogenetics. , 2004, Systematic biology.
[36] H. White. Maximum Likelihood Estimation of Misspecified Models , 1982 .
[37] Aki Vehtari,et al. Sparsity information and regularization in the horseshoe and other shrinkage priors , 2017, 1707.01694.
[38] N. Hjort,et al. On Bayesian consistency , 2001 .
[39] P. Bühlmann,et al. Analyzing Bagging , 2001 .
[40] F. Lutzoni,et al. Bayes or bootstrap? A simulation study comparing the performance of Bayesian Markov chain Monte Carlo sampling and bootstrapping in assessing phylogenetic confidence. , 2003, Molecular biology and evolution.
[41] H. Künsch. The Jackknife and the Bootstrap for General Stationary Observations , 1989 .
[42] D. Schaid,et al. From genome-wide associations to candidate causal variants by statistical fine-mapping , 2018, Nature Reviews Genetics.
[43] David M. Blei,et al. Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models , 2014 .
[44] Thijs van Ommen,et al. Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It , 2014, 1412.3730.
[45] Jiqiang Guo,et al. Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.
[46] A. Bhattacharya,et al. Bayesian fractional posteriors , 2016, The Annals of Statistics.
[47] Raul Cano. On The Bayesian Bootstrap , 1992 .
[48] Derrick J. Zwickl,et al. Phylogenetic relationships of the dwarf boas and a comparison of Bayesian and bootstrap measures of phylogenetic support. , 2002, Molecular phylogenetics and evolution.
[49] C. Holmes,et al. Assigning a value to a power likelihood in a general Bayesian model , 2017, 1701.08515.
[50] Xiao-Li Meng,et al. POSTERIOR PREDICTIVE ASSESSMENT OF MODEL FITNESS VIA REALIZED DISCREPANCIES , 1996 .
[51] Cosma Rohilla Shalizi,et al. Philosophy and the practice of Bayesian statistics. , 2010, The British journal of mathematical and statistical psychology.
[52] Maxim Teslenko,et al. MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space , 2012, Systematic biology.
[53] Stephen G. Walker,et al. Bayesian Nonparametric Inference for the Power Likelihood , 2013 .
[54] R. Royall,et al. Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions , 2003 .
[55] David R. Cox,et al. Role of Models in Statistical Analysis , 1990 .
[56] Enno Mammen,et al. Bootstrap, wild bootstrap, and asymptotic normality , 1992 .
[57] David B. Dunson,et al. Comparing and Weighting Imperfect Models Using D-Probabilities , 2016, Journal of the American Statistical Association.
[58] Hirohisa Kishino,et al. Very fast algorithms for evaluating the stability of ML and Bayesian phylogenetic trees from sequence data. , 2002, Genome informatics. International Conference on Genome Informatics.
[59] William J. Browne,et al. Bayesian and likelihood-based methods in multilevel modeling 1 A comparison of Bayesian and likelihood-based methods for fitting multilevel models , 2006 .
[60] Ryan Martin,et al. Calibrating general posterior credible regions , 2015, Biometrika.
[61] George E. P. Box,et al. Sampling and Bayes' inference in scientific modelling and robustness , 1980 .
[62] Christian P. Robert,et al. The Bayesian choice , 1994 .
[63] Andrew R. Barron,et al. Information-theoretic asymptotics of Bayes methods , 1990, IEEE Trans. Inf. Theory.
[64] D. Rubin. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .
[65] T. Buckley,et al. Model misspecification and probabilistic tests of topology: evidence from empirical data sets. , 2002, Systematic biology.
[66] Michael I. Jordan,et al. Covariances, Robustness, and Variational Bayes , 2017, J. Mach. Learn. Res..
[67] Qian M. Zhou,et al. Information Ratio Test for Model Misspecification in Quasi-Likelihood Inference , 2012 .
[68] Ryan Martin,et al. Likelihood-free Bayesian inference on the minimum clinically important difference , 2015, 1501.01840.
[69] Rianne de Heide,et al. Safe-Bayesian Generalized Linear Regression , 2019, AISTATS.
[70] Peter Grünwald,et al. The Safe Bayesian - Learning the Learning Rate via the Mixability Gap , 2012, ALT.
[71] John K Kruschke,et al. Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[72] P. Diaconis,et al. Updating Subjective Probability , 1982 .
[73] G. Imbens,et al. Nonparametric Applications of Bayesian Inference , 1996 .
[74] Martin Raič,et al. Normal Approximation by Stein ’ s Method , 2003 .