Journal ArticleDOI
Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
TLDR
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.Abstract:
A critical issue for users of Markov chain Monte Carlo (MCMC) methods in applications is how to determine when it is safe to stop sampling and use the samples to estimate characteristics of the distribution of interest. Research into methods of computing theoretical convergence bounds holds promise for the future but to date has yielded relatively little of practical use in applied work. Consequently, most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. After giving a brief overview of the area, we provide an expository review of 13 convergence diagnostics, describing the theoretical basis and practical implementation of each. We then compare their performance in two simple models and conclude that all of the methods can fail to detect the sorts of convergence failure that they were designed to identify. We thus recommend a combination of strategies aimed at evaluating and accelerating MCMC sampler convergence, including ap...read more
Citations
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Journal ArticleDOI
A novel approach for estimating location and scale specific fishing exploitation rates of eastern Bering Sea walleye pollock (Theragra chalcogramma)
TL;DR: In this article, acoustic data collected opportunistically from eastern Bering Sea (EBS) walleye pollock (Theragra chalcogramma) fishing vessels were used within spatially explicit Leslie depletion models to estimate local exploitation rates.
Posted Content
Convergence diagnostics for MCMC draws of a categorical variable
Benjamin Deonovic,Brian J Smith +1 more
TL;DR: Two convergence diagnostic methods are considered which are appropriate for MCMC data and utilize chi-squared test statistics for dependent data and are evaluated under various simulations.
Dissertation
Reconstruction of gene regulatory networks from postgenomic data
TL;DR: This thesis extends and improves existing methods to include biological prior knowledge under the Bayesian approach in order to increase the accuracy of the predicted networks and it quantifies to what extent the reconstruction accuracy can be improved in this way.
Book ChapterDOI
Geographical Distribution of Cardiovascular Mortality in Comunidad Valenciana (Spain)
Juan Ferrándiz,J.J. Abellán,Antonio López,Pilar Sanmartín,Hermelinda Vanaclocha,Oscar Zurriaga,Miguel A. Martinez-Beneito,Inmaculada Melchor,Julia Calabuig +8 more
TL;DR: Comunidad Valenciana is one of the seventeen autonomous regions into which Spain is divided as discussed by the authors, and it is located on the east coast of Spain, next to the Mediterranean sea, with an area of 23,255 km2 and with 4,009,329 inhabitants in 1996.
Proceedings ArticleDOI
Framework for Quantification and Risk Analysis for Layered Uncertainty using Optimization: NASA UQ Challenge
TL;DR: For uncertainty characterization, an MCMC based Bayesian approach and a CDF Matching method are compared and found to give similar results; thus increasing confidence in the methods and their repeatability.
References
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Equation of state calculations by fast computing machines
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Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
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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
Robust Locally Weighted Regression and Smoothing Scatterplots
TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.