Reflections on Bayesian inference and Markov chain Monte Carlo
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[1] Nicholas G. Tawn,et al. Skew brownian motion and complexity of the alps algorithm , 2020, Journal of Applied Probability.
[2] Edward I. George,et al. Bayes and big data: the consensus Monte Carlo algorithm , 2016, Big Data and Information Theory.
[3] Taylor G. Asher,et al. High-fidelity hurricane surge forecasting using emulation and sequential experiments , 2021 .
[4] J. Ioannidis,et al. COVID-19 antibody seroprevalence in Santa Clara County, California , 2021, International journal of epidemiology.
[5] Evgeny Levi,et al. Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods , 2019, Bayesian Analysis.
[6] I. Heid,et al. Analysis of COVID-19 case numbers: adjustment for diagnostic misclassification on the example of German case reporting data , 2020 .
[7] Igor Burstyn,et al. Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA , 2020, BMC Medical Research Methodology.
[8] A. Gelman,et al. Bayesian Analysis of Tests with Unknown Specificity and Sensitivity , 2020, medRxiv.
[9] P. Gustafson,et al. Bayesian adjustment for preferential testing in estimating the COVID-19 infection fatality rate: Theory and methods , 2020, 2005.08459.
[10] A. Mertens,et al. Substantial underestimation of SARS-CoV-2 infection in the United States , 2020, Nature Communications.
[11] P. Gustafson,et al. Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: Case study of COVID-19 in Alberta, Canada and Philadelphia, USA , 2020, medRxiv.
[12] Daniel Simpson,et al. Asynchronous Gibbs Sampling , 2015, AISTATS.
[13] Alexandre Bouchard-Cot'e,et al. Blang: Bayesian declarative modelling of arbitrary data structures. , 2019 .
[14] Wu Changye,et al. Parallelising MCMC via Random Forests , 2019 .
[15] Yanan Fan,et al. Handbook of Approximate Bayesian Computation , 2018 .
[16] Huidong Jin,et al. Emulator-enabled approximate Bayesian computation (ABC) and uncertainty analysis for computationally expensive groundwater models , 2018, Journal of Hydrology.
[17] Christopher C. Drovandi,et al. Approximating the Likelihood in ABC , 2018, Handbook of Approximate Bayesian Computation.
[18] Michael Gertz,et al. Numerically stable parallel computation of (co-)variance , 2018, SSDBM.
[19] Christopher C Drovandi,et al. ABC and Indirect Inference , 2018, Handbook of Approximate Bayesian Computation.
[20] S. A. Sisson,et al. Overview of Approximate Bayesian Computation , 2018, 1802.09720.
[21] Chris Sherlock,et al. Merging MCMC Subposteriors through Gaussian-Process Approximations , 2016, Bayesian Analysis.
[22] R. Kohn,et al. Speeding Up MCMC by Efficient Data Subsampling , 2014, Journal of the American Statistical Association.
[23] James P. Hobert,et al. Asymptotically Stable Drift and Minorization for Markov Chains with Application to Albert and Chib's Algorithm , 2017 .
[24] J. Rosenthal,et al. Complexity results for MCMC derived from quantitative bounds , 2017, The Annals of Applied Probability.
[25] David T. Frazier,et al. Bayesian Synthetic Likelihood , 2017, 2305.05120.
[26] C. Robert,et al. Inference in generative models using the Wasserstein distance , 2017, 1701.05146.
[27] Jiqiang Guo,et al. Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.
[28] Gareth O. Roberts,et al. Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits , 2016, Journal of Applied Probability.
[29] Trevor Campbell,et al. Coresets for Scalable Bayesian Logistic Regression , 2016, NIPS.
[30] Radu V. Craiu,et al. Likelihood inflating sampling algorithm , 2016, 1605.02113.
[31] Haavard Rue,et al. Bayesian Computing with INLA: A Review , 2016, 1604.00860.
[32] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[33] Dennis Prangle,et al. Summary Statistics in Approximate Bayesian Computation , 2015, 1512.05633.
[34] B. Rajaratnam,et al. MCMC-Based Inference in the Era of Big Data: A Fundamental Analysis of the Convergence Complexity of High-Dimensional Chains , 2015, 1508.00947.
[35] Martin J. Wainwright,et al. On the Computational Complexity of High-Dimensional Bayesian Variable Selection , 2015, ArXiv.
[36] Paul Gustafson,et al. Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data , 2015 .
[37] Dieter Gerten,et al. Emulating global climate change impacts on crop yields , 2015 .
[38] C. Andrieu,et al. Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms , 2012, 1210.1484.
[39] Luke Bornn,et al. One Pseudo-Sample is Enough in Approximate Bayesian Computation MCMC , 2014 .
[40] Chong Wang,et al. Asymptotically Exact, Embarrassingly Parallel MCMC , 2013, UAI.
[41] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[42] Xiangyu Wang,et al. Parallelizing MCMC via Weierstrass Sampler , 2013, 1312.4605.
[43] J. Rosenthal,et al. Convergence rate of Markov chain methods for genomic motif discovery , 2013, 1303.2814.
[44] Julien Cornebise,et al. On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo , 2011, Statistical applications in genetics and molecular biology.
[45] Anthony Lee,et al. On the choice of MCMC kernels for approximate Bayesian computation with SMC samplers , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).
[46] Paul Fearnhead,et al. Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation , 2012 .
[47] Jean-Michel Marin,et al. Approximate Bayesian computational methods , 2011, Statistics and Computing.
[48] Anthony N. Pettitt,et al. Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation , 2012 .
[49] Elizabeth Thompson,et al. MCMC in the Analysis of Genetic Data on Related Individuals , 2011 .
[50] Andrew Gelman,et al. Handbook of Markov Chain Monte Carlo , 2011 .
[51] Radford M. Neal. MCMC Using Hamiltonian Dynamics , 2011, 1206.1901.
[52] S. Wood. Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.
[53] Cliburn Chan,et al. Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures , 2010, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[54] Andrew Thomas,et al. The BUGS project: Evolution, critique and future directions , 2009, Statistics in medicine.
[55] D. Woodard,et al. Conditions for Rapid and Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions , 2009, 0906.2341.
[56] D. Woodard,et al. Sufficient Conditions for Torpid Mixing of Parallel and Simulated Tempering , 2009 .
[57] C. Andrieu,et al. The pseudo-marginal approach for efficient Monte Carlo computations , 2009, 0903.5480.
[58] Mark M. Tanaka,et al. Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.
[59] P. Baxendale. Renewal theory and computable convergence rates for geometrically ergodic Markov chains , 2005, math/0503515.
[60] Darren J. Wilkinson,et al. Parallel Bayesian Computation , 2005 .
[61] Galin L. Jones,et al. Sufficient burn-in for Gibbs samplers for a hierarchical random effects model , 2004, math/0406454.
[62] Kathryn B. Laskey,et al. Population Markov Chain Monte Carlo , 2004, Machine Learning.
[63] Paul Marjoram,et al. Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[64] C. Manski. Partial Identification of Probability Distributions , 2003 .
[65] J. Rosenthal. QUANTITATIVE CONVERGENCE RATES OF MARKOV CHAINS: A SIMPLE ACCOUNT , 2002 .
[66] J. Rosenthal,et al. Optimal scaling for various Metropolis-Hastings algorithms , 2001 .
[67] Gareth O. Roberts,et al. Corrigendum to : Bounds on regeneration times and convergence rates for Markov chains , 2001 .
[68] Galin L. Jones,et al. Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo , 2001 .
[69] Andrew Thomas,et al. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..
[70] R. Tweedie,et al. Bounds on regeneration times and convergence rates for Markov chains fn1 fn1 Work supported in part , 1999 .
[71] Sally Rosenthal,et al. Parallel computing and Monte Carlo algorithms , 1999 .
[72] J. Rosenthal,et al. Optimal scaling of discrete approximations to Langevin diffusions , 1998 .
[73] A. Gelman,et al. Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .
[74] Jeffrey S. Rosenthal,et al. Analysis of the Gibbs Sampler for a Model Related to James-stein Estimators , 2007 .
[75] J. Rosenthal. RATES OF CONVERGENCE FOR GIBBS SAMPLING FOR VARIANCE COMPONENT MODELS , 1995 .
[76] J. Rosenthal. Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo , 1995 .
[77] S. Meyn,et al. Computable Bounds for Geometric Convergence Rates of Markov Chains , 1994 .
[78] Walter R. Gilks,et al. A Language and Program for Complex Bayesian Modelling , 1994 .
[79] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[80] Mark J. Schervish. Applications of Parallel Computation to Statistical Inference , 1988 .
[81] Stephen A. Cook,et al. The complexity of theorem-proving procedures , 1971, STOC.
[82] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[83] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[84] D. Woodard,et al. Conditions for Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions , 2022 .