Markov Chain Monte Carlo Methods, Survey with Some Frequent Misunderstandings
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[1] Eric Moulines,et al. Adaptive parallel tempering algorithm , 2012, 1205.1076.
[2] M. Girolami,et al. Riemann manifold Langevin and Hamiltonian Monte Carlo methods , 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[3] Jean-Marie Cornuet,et al. Lack of confidence in ABC model choice , 2011, 1102.4432.
[4] Heikki Haario,et al. Adaptive proposal distribution for random walk Metropolis algorithm , 1999, Comput. Stat..
[5] C. Andrieu,et al. The pseudo-marginal approach for efficient Monte Carlo computations , 2009, 0903.5480.
[6] G. Roberts,et al. Kinetic energy choice in Hamiltonian/hybrid Monte Carlo , 2017, Biometrika.
[7] J. Møller,et al. An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants , 2006 .
[8] Paul Fearnhead,et al. Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation , 2012 .
[9] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[10] D. Rubin. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician , 1984 .
[11] J. Berger. Statistical Decision Theory and Bayesian Analysis , 1988 .
[12] Richard G. Everitt,et al. Likelihood-free estimation of model evidence , 2011 .
[13] George Casella,et al. A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data , 2008, 0808.2902.
[14] M. Blum. Approximate Bayesian Computation: A Nonparametric Perspective , 2009, 0904.0635.
[15] Yanan Fan,et al. Handbook of Approximate Bayesian Computation , 2018 .
[16] S. Sisson,et al. A comparative review of dimension reduction methods in approximate Bayesian computation , 2012, 1202.3819.
[17] Peter W. Glynn,et al. Exact estimation for Markov chain equilibrium expectations , 2014, Journal of Applied Probability.
[18] Jun S. Liu,et al. Covariance Structure and Convergence Rate of the Gibbs Sampler with Various Scans , 1995 .
[19] J. Heng,et al. Unbiased Hamiltonian Monte Carlo with couplings , 2017, Biometrika.
[20] Paul Fearnhead,et al. On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators , 2015, 1506.03481.
[21] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[22] D. Woodard,et al. Sufficient Conditions for Torpid Mixing of Parallel and Simulated Tempering , 2009 .
[23] J. Cunningham,et al. Expectation Propagation as a Way of Life , 2020 .
[24] A. Pettitt,et al. Approximate Bayesian computation using indirect inference , 2011 .
[25] P. Jacob,et al. Unbiased Markov chain Monte Carlo methods with couplings , 2020, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[26] B. Efron,et al. Stein's Estimation Rule and Its Competitors- An Empirical Bayes Approach , 1973 .
[27] Michael Betancourt,et al. A Conceptual Introduction to Hamiltonian Monte Carlo , 2017, 1701.02434.
[28] P. Donnelly,et al. Inferring coalescence times from DNA sequence data. , 1997, Genetics.
[29] Christian P Robert,et al. Molecular Ecology Ressources – subject area: Methodological Advances 1 2 Estimation of demo-genetic model probabilities with Approximate Bayesian 3 Computation using linear discriminant analysis on summary statistics , 2012 .
[30] S. Coles,et al. Inference for Stereological Extremes , 2007 .
[31] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[32] Michael I. Jordan,et al. Bayesian parameter estimation via variational methods , 2000, Stat. Comput..
[33] Arnaud Doucet,et al. Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach , 2014, ICML.
[34] Jun S. Liu,et al. Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes , 1994 .
[35] Christian P. Robert,et al. Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.
[36] S. Wood. Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.
[37] Gareth W. Peters,et al. Likelihood-free Bayesian inference for α-stable models , 2012, Comput. Stat. Data Anal..
[38] P. Diaconis,et al. The sample size required in importance sampling , 2015, 1511.01437.
[39] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[40] R. Tweedie,et al. Exponential Convergence of Langevin Diiusions and Their Discrete Approximations , 1997 .
[41] Christian P. Robert,et al. Bayesian computation: a summary of the current state, and samples backwards and forwards , 2015, Statistics and Computing.
[42] J. Hammersley. SIMULATION AND THE MONTE CARLO METHOD , 1982 .
[43] Rong Chen,et al. A Theoretical Framework for Sequential Importance Sampling with Resampling , 2001, Sequential Monte Carlo Methods in Practice.
[44] Jeffrey S. Rosenthal,et al. Coupling and Ergodicity of Adaptive MCMC , 2007 .
[45] G. Casella,et al. Rao-Blackwellisation of sampling schemes , 1996 .
[46] É. Moulines,et al. Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm , 2015, 1507.05021.
[47] Jiqiang Guo,et al. Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.
[48] Christophe Andrieu,et al. Establishing some order amongst exact approximations of MCMCs , 2014, 1404.6909.
[49] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[50] Christophe Andrieu,et al. Model criticism based on likelihood-free inference, with an application to protein network evolution , 2009, Proceedings of the National Academy of Sciences.
[51] K. Mengersen,et al. Robustness of ranking and selection rules using generalised g-and-k distributions , 1997 .
[52] M. Feldman,et al. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. , 1999, Molecular biology and evolution.
[53] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[54] Martyn Plummer. Cuts in Bayesian graphical models , 2015, Stat. Comput..
[55] Christian P. Robert,et al. Componentwise approximate Bayesian computation via Gibbs-like steps , 2019, Biometrika.
[56] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[57] A. Kennedy,et al. Hybrid Monte Carlo , 1988 .
[58] Joseph Fourier,et al. Approximate Bayesian Computation: a non-parametric perspective , 2013 .
[59] Christian P. Robert,et al. Better together? Statistical learning in models made of modules , 2017, 1708.08719.
[60] J.-M. Marin,et al. Relevant statistics for Bayesian model choice , 2011, 1110.4700.
[61] Djc MacKay,et al. Slice sampling - Discussion , 2003 .
[62] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[63] R. B. Potts. Some generalized order-disorder transformations , 1952, Mathematical Proceedings of the Cambridge Philosophical Society.
[64] O. Cappé,et al. Markov Chain Monte Carlo: 10 Years and Still Running! , 2000 .
[65] Paul Marjoram,et al. Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[66] Christian P Robert,et al. Bayesian computation via empirical likelihood , 2012, Proceedings of the National Academy of Sciences.
[67] D. Dunson,et al. The Hastings algorithm at fifty , 2020 .
[68] David T. Frazier,et al. Asymptotic properties of approximate Bayesian computation , 2016, Biometrika.
[69] A. Pettitt,et al. Marginal likelihood estimation via power posteriors , 2008 .
[70] A. Futschik,et al. A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation , 2012, Genetics.
[71] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.