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
Comparisons and Uncertainty in Fat and Adipose Tissue Estimation Techniques: The Northern Elephant Seal as a Case Study
Lisa K. Schwarz,Stella Villegas-Amtmann,Roxanne S. Beltran,Daniel P. Costa,Chandra Goetsch,Luis A. Hückstädt,Jennifer L. Maresh,Sarah H. Peterson +7 more
TL;DR: A modified cones method is proposed that can isolate blubber from non-blubber adipose tissue and separate fat into skin, blubbers, and core compartments and allow for more accurate quantification of the various tissue masses and may also be transferrable to other species.
Journal ArticleDOI
Metamodel-based Markov-Chain-Monte-Carlo parameter inversion applied in eddy current flaw characterization
Caifang Cai,Roberto Miorelli,Marc Lambert,Thomas Rodet,Dominique Lesselier,Pierre-Emile Lhuillier +5 more
TL;DR: In this paper, a computationally-cheap surrogate forward model was introduced into a MCMC algorithm for eddy current flaw characterization, which can deal with complicated forward models and does not reduce to only providing the parameters sought.
Journal ArticleDOI
The Dynamics of Expected Returns: Evidence from Multi-Scale Time Series Modeling
Daniele Bianchi,Andrea Tamoni +1 more
TL;DR: In this article, the authors show that low-order autoregression models for short-term expected returns imply long-term dynamics that have a (too) fast vanishing persistence when compared with the evidence from long-horizon predictive regressions.
Proceedings ArticleDOI
Steganography using Gibbs random fields
Tomas Filler,Jessica Fridrich +1 more
TL;DR: This work provides a general framework and practical methods for embedding with an arbitrary distortion function that does not have to be additive over pixels and thus can consider interactions among embedding changes.
Journal ArticleDOI
Efficient and accurate approximate Bayesian inference with an application to insurance data
TL;DR: Efficient and accurate Bayesian Markov chain Monte Carlo methodology is proposed for the estimation of event rates under an overdispersed Poisson distribution, based on a log-normal/gamma mixture density that closely approximates the conditional distribution of the Poisson parameters.
References
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Equation of state calculations by fast computing machines
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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.
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Monte Carlo Sampling Methods Using Markov Chains and Their Applications
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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.
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.