Handbook of Markov Chain Monte Carlo
Reads0
Chats0
TLDR
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
More filters
Journal ArticleDOI
A Bayesian nonparametric meta‐analysis model
TL;DR: This work proposes a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions.
Posted Content
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
TL;DR: In this paper, a covariance-controlled adaptive Langevin thermostat is proposed to dissipate parameter-dependent noise while maintaining a desired target distribution, which achieves a substantial speedup over popular alternative schemes for large scale machine learning applications.
Journal ArticleDOI
Gaussian process models for mortality rates and improvement factors
TL;DR: The authors developed a Gaussian process (GP) framework for modeling mortality rates and mortality improvement factors, which is well suited for updating projections when newly available data arrives, and for dealing with edge issues where credibility is lower.
Journal ArticleDOI
Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces
TL;DR: In this article, an active subspace method is used to find directions in the parameter space that dominate the Bayesian update from the prior to the posterior distribution in order to effectively reduce the dimension of the problem and for computational efficiency.
Posted Content
Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation.
TL;DR: This work proposes new unbiased estimators of the logarithm of ratios of normalizing constants of Markov chain Monte Carlo estimators with path sampling, also called thermodynamic integration, and proposes unbiased estimator of the Bayesian cross-validation criterion.
References
More filters
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.