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Markov chain monte carlo convergence diagnostics
 JASA
, 1996
"... 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 ..."
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Cited by 371 (6 self)
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review of thirteen convergence diagnostics, describing the theoretical basis and practical implementation of each. We then compare their performance in two simple models and conclude that all the methods can fail to detect the sorts of convergence failure they were designed to identify. We thus recommend
Evaluating the Accuracy of SamplingBased Approaches to the Calculation of Posterior Moments
 IN BAYESIAN STATISTICS
, 1992
"... Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accurac ..."
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Cited by 602 (12 self)
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accuracy of the approximations to the expected value of functions of interest under the posterior. In this paper methods from spectral analysis are used to evaluate numerical accuracy formally and construct diagnostics for convergence. These methods are illustrated in the normal linear model
Abstract Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
"... 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 ..."
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an expository review of thirteen convergence diagnostics, describing the theoretical basis and practical implementation of each. We then compare their performance in two simple models and conclude that all the methods can fail to detect the sorts of convergence failure they were designed to identify. We thus
Thirteen Supplemental Figures.
"... Table S3. The fraction of sense and antisense piRNAs that were uniquely bound to each PIWI protein. Table S4. Probes for Northern hybridization Table S5. Primers for quantitative PCR. Table S6. Fly stocks used in this study. ..."
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Table S3. The fraction of sense and antisense piRNAs that were uniquely bound to each PIWI protein. Table S4. Probes for Northern hybridization Table S5. Primers for quantitative PCR. Table S6. Fly stocks used in this study.
AWTY (Are We There Yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics
, 2007
"... Summary: A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations—convergence diagnostics—in phylogenetics is still uncommon. Here we present a simple t ..."
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Cited by 105 (4 self)
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Summary: A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations—convergence diagnostics—in phylogenetics is still uncommon. Here we present a simple
The Number of Iterations, Convergence Diagnostics and Generic Metropolis Algorithms
 In Practical Markov Chain Monte Carlo (W.R. Gilks, D.J. Spiegelhalter and
, 1995
"... Introduction In order to use Markov chain Monte Carlo, MCMC, it is necessary to determine how long the simulation needs to be run. It is also a good idea to discard a number of initial "burnin " simulations, since from an arbitrary starting point it would be unlikely that the initial simu ..."
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Cited by 43 (3 self)
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Introduction In order to use Markov chain Monte Carlo, MCMC, it is necessary to determine how long the simulation needs to be run. It is also a good idea to discard a number of initial "burnin " simulations, since from an arbitrary starting point it would be unlikely that the initial simulations came from the stationary distribution intended for the Markov chain. Also, consecutive simulations from Markov chains are dependent, sometimes highly so. Since saving all simulations can require a large amount of storage, researchers using MCMC sometimes prefer saving only every third, fifth, tenth, etc. simulation, especially if the chain is highly dependent. This is sometimes referred to as thinning the chain. While neither burnin nor thinning are mandatory practices, they both reduce the amount of data saved from a MCMC run. In this chapter, we outline a way of determining in advance the number of iterations needed for a given level of precision in a MCMC algorithm.
Review of the Cassidinae of Ecuador, with a description of thirteen new species (Coleoptera: Chrysomelidae
 Genus
, 1998
"... ABSTRACT. A complete list of 200 species of the subfamily Cassidinae recorded from Ecuador is given, 44 for the first time, and 13 of them are new to the science: ..."
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Cited by 3 (1 self)
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ABSTRACT. A complete list of 200 species of the subfamily Cassidinae recorded from Ecuador is given, 44 for the first time, and 13 of them are new to the science:
Results 1  10
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147,342