[Practical Markov Chain Monte Carlo]: Rejoinder: Replication without Contrition
Andrew Gelman,Donald B. Rubin +1 more
TLDR
In this paper, the convergence of MCMC samples is discussed and a complete list of the commentaries on these articles is shown on the next page, along with a list of commentaries for each article.Abstract:
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Institute of Mathematical Statistics is collaborating with JSTOR to digitize, preserve and extend access to Statistical Science. This article is from a volume of Statistical Science (1992; 7(4)) on the convergence of MCMC samples. The two main articles are: A complete list of the commentaries on these articles is shown on the next page.read more
Citations
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Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
TL;DR: All of the methods in this work can fail to detect the sorts of convergence failure that they were designed to identify, so a combination of strategies aimed at evaluating and accelerating MCMC sampler convergence are recommended.
Journal ArticleDOI
Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of Drosophila pseudoobscura and D. persimilis
Jody Hey,Rasmus Nielsen +1 more
TL;DR: A Markov chain Monte Carlo method for estimating the posterior probability distribution of model parameters is applied to a large multilocus data set from Drosophila pseudoobscura and D. persimilis, with considerable variation in gene flow estimates among loci, in both directions between the species.
Journal ArticleDOI
Particle filter-based data assimilation for a three-dimensional biological ocean model and satellite observations
TL;DR: It is shown that SIR is suitable for satellite data assimilation into biological models and that both extensions, the smoother and state-augmentation, are required for robust results and improved fit to the observations.
Journal ArticleDOI
Bayesian analysis of nested logit model by Markov chain Monte Carlo
Kajal Lahiri,Jian Gao +1 more
TL;DR: In this paper, a Markov chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework is developed for fast mixing, where appropriate heating target and reparameterization techniques are adopted to ensure that the chain converges to its target distribution.
Journal ArticleDOI
Comparison of nonstationary models in analyzing bivariate flood frequency at the Three Gorges Dam
Xu Zhang,Kai Duan,Qianjin Dong +2 more
TL;DR: The results suggest that nonstationary models are clearly superior to stationary models with respect to model performance based on the deviance information criterion (DIC), and explicitly incorporating climate indices as explanatory variables of flood frequency distribution can significantly improve model performance and reduce uncertainty but at the cost of increased model complexity.
References
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TL;DR: A survey of the main methods in non-uniform random variate generation can be found in this article, where the authors provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes and Markov chain methods.