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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Insights into the content and spatial distribution of dust from the integrated spectral properties of galaxies
TL;DR: In this article, the authors acknowledge the support of the NSF via the grant AST 07-08849 and of the Agence Nationale de la Recherche via the Chaire d'Excellence ANR-10-CEXC-004-01.
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Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach.
TL;DR: In this paper, the authors investigate the underlying causes of parameter non-identifiability and discuss straightforward methods for determining when parameters of simple models can be inferred accurately, for models of even modest complexity, and present a method based in Bayesian inference that can be used to establish the reliability of parameter estimates.
Posted Content
Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians
Ari Pakman,Liam Paninski +1 more
TL;DR: In this paper, a Hamiltonian Monte Carlo (HMMC) algorithm is proposed to sample from multivariate Gaussian distributions in which the target space is constrained by linear and quadratic inequalities or products thereof.
Journal ArticleDOI
Hilbert space methods for reduced-rank Gaussian process regression
Arno Solin,Simo Särkkä +1 more
TL;DR: In this article, an approximate series expansion of the covariance function in terms of an eigenfunction expansion of Laplace operator in a compact subset of the Gaussian process is proposed.
Journal ArticleDOI
Multisystem Bayesian constraints on the transport coefficients of QCD matter
D. Everett,Weiyao Ke,Jean-François Paquet,G. Vujanovic,Steffen A. Bass,Lipei Du,Charles Gale,M. Heffernan,Ulrich Heinz,D. Liyanage,Matthew Luzum,Abhijit Majumder,M. McNelis,Chun Shen,Y. Xu,Aaron Angerami,Shanshan Cao,Yi Chen,J. Coleman,L. Cunqueiro,T. Dai,R. Ehlers,Hannah Elfner,W. Fan,Rainer J. Fries,F. Garza,Yayun He,Barbara Jacak,Peter Martin Jacobs,S. Jeon,B. Kim,M. Kordell,Ajay Kumar,Simon Mak,J. Mulligan,Christine Nattrass,D. Oliinychenko,C. Park,J. Putschke,Gunther Roland,Björn Schenke,L. Schwiebert,Antonio Carlos Oliveira da Silva,C. Sirimanna,R. A. Soltz,Yasuki Tachibana,Xin-Nian Wang,Robert L. Wolpert +47 more
TL;DR: Eden et al. as discussed by the authors studied the properties of the strongly coupled quark-gluon plasma with a multistage model of heavy-ion collisions that combines the TRENTo initial condition ansatz, free-streaming, viscous relativistic hydrodynamics, and a hadronic transport.
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.