Handbook of Markov Chain Monte Carlo
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A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data and its applications in environmental epidemiology, educational research, and fisheries science are studied.Abstract:
Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng Spatial point processes, Mark Huber The data augmentation algorithm: Theory and methodology, James P. Hobert Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox Statistical ecology, Ruth King Gaussian random field models for spatial data, Murali Haran Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger MCMC for state space models, Paul Fearnhead MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens Applications of MCMC in fisheries science, Russell B. Millar Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Westernread more
Citations
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Quantifying within-school SARS-CoV-2 transmission and the impact of lateral flow testing in secondary schools in England
Trystan Leng,Edward M. Hill,Alexander Holmes,Alexander Holmes,Emma Southall,Emma Southall,Robin N Thompson,Michael J. Tildesley,Matthew James Keeling,Louise Dyson +9 more
TL;DR: In this paper, the authors quantify within-school transmission of SARS-CoV-2 in secondary schools in England, and the impact of twice weekly mass testing on within school transmission and the potential impact of alternative strategies to reduce pupil absences.
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Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler
TL;DR: A novel implementation of Bayesian cosmic microwave background (CMB) component separation using the No-U-Turn Sampler (NUTS), a gradient-based sampling algorithm that allows it to fit the more sophisticated hierarchical foreground model that would likely be intractable with non-gradient- based sampling algorithms.
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Efficient parameter estimation for a methane hydrate model with active subspaces
Mario Teixeira Parente,Steven Mattis,Shubhangi Gupta,Christian Deusner,Barbara Wohlmuth,Barbara Wohlmuth +5 more
TL;DR: In this article, Bayesian inference of the parameters of a state-of-the-art mathematical model for methane hydrates based on experimental data from a triaxial compression test with gas hydrate-bearing sand is performed in an efficient way by utilizing active subspaces.
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Overview and introduction to development of non-ergodic earthquake ground-motion models
TL;DR: In this paper , the authors provide an overview and introduction to the development of non-ergodic ground-motion models, GMMs, with an emphasis on Gaussian process regression.
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Pseudo-extended Markov chain Monte Carlo
TL;DR: The pseudo-extended MCMC method as mentioned in this paper augments the state-space of the posterior using pseudo-samples as auxiliary variables, which allows the MCMC sampler to easily move between well-separated posterior modes.
References
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Monte Carlo Sampling Methods Using Markov Chains and Their Applications
TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
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Inference from Iterative Simulation Using Multiple Sequences
Andrew Gelman,Donald B. Rubin +1 more
TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
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Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
TL;DR: In this article, the authors propose a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive.
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Variable selection via Gibbs sampling
TL;DR: In this paper, the Gibbs sampler is used to indirectly sample from the multinomial posterior distribution on the set of possible subset choices to identify the promising subsets by their more frequent appearance in the Gibbs sample.
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An adaptive Metropolis algorithm
TL;DR: An adaptive Metropolis (AM) algorithm, where the Gaussian proposal distribution is updated along the process using the full information cumulated so far, which establishes here that it has the correct ergodic properties.