Results 1  10
of
517
Coda Convergence Diagnosis and Output Analysis software for Gibbs sampler output: Version 0.4
, 1995
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The TimeVarying Volatility of Macroeconomic Fluctuations
, 2006
"... In this paper we investigate the sources of the important shifts in the volatility of U.S. macroeconomic variables in the postwar period. To this end, we propose the estimation of DSGE models allowing for time variation in the volatility of the structural innovations. We apply our estimation strate ..."
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Cited by 156 (5 self)
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In this paper we investigate the sources of the important shifts in the volatility of U.S. macroeconomic variables in the postwar period. To this end, we propose the estimation of DSGE models allowing for time variation in the volatility of the structural innovations. We apply our estimation strategy to a largescale model of the business cycle and find that investment specific technology shocks account for most of the sharp decline in volatility of the last two decades.
Decomposable Graphical Gaussian Model Determination
, 1999
"... We propose a methodology for Bayesian model determination in decomposable graphical gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obt ..."
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Cited by 107 (12 self)
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We propose a methodology for Bayesian model determination in decomposable graphical gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obtained by marginalisation from the prior conditional on the complete graph. We explore alternative structures for the hyperparameters of the latter, and their consequences for the model. Model determination is carried out by implementing a reversible jump MCMC sampler. In particular, the dimensionchanging move we propose involves adding or dropping an edge from the graph. We characterise the set of moves which preserve the decomposability of the graph, giving a fast algorithm for maintaining the junction tree representation of the graph at each sweep. As state variable, we propose to use the incomplete variancecovariance matrix, containing only the elements for which the correspondi...
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 99 (3 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 tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here complements convergence diagnostics already available in other software packages primarily designed for other applications of MCMC. Importantly, the common practice of using traceplots of a single parameter or summary statistic, such as the likelihood score of sampled trees, can be misleading for assessing the success of a phylogenetic MCMC simulation.
Assessing Convergence of Markov Chain Monte Carlo Algorithms
 Statistics and Computing
, 1997
"... We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The ..."
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Cited by 87 (11 self)
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We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are provided for particular classes of problems. 1 Introduction There are many important implementational issues associated with MCMC methods. These include (amongst others) the choice of sampler, the number of independent replications to be run, the choice of starting values and both estimation and efficiency problems. In practice, we use ergodic averages over realisations of a Markov chain to estimate functionals of interest. In order to reduce the possibility of bias caused by the effect of starting values, iterates within an initial transient phase or burn in period are usually discarded. One o...
Markov chain Monte Carlo: Can we trust the third significant figure
 University of Minnesota, School of Statistics
, 2007
"... Abstract. Current reporting of results based on Markov chain Monte Carlo computations could be improved. In particular, a measure of the accuracy of the resulting estimates is rarely reported. Thus we have little ability to objectively assess the quality of the reported estimates. We address this is ..."
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Cited by 68 (25 self)
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Abstract. Current reporting of results based on Markov chain Monte Carlo computations could be improved. In particular, a measure of the accuracy of the resulting estimates is rarely reported. Thus we have little ability to objectively assess the quality of the reported estimates. We address this issue in that we discuss why Monte Carlo standard errors are important, how they can be easily calculated in Markov chain Monte Carlo and how they can be used to decide when to stop the simulation. We compare their use to a popular alternative in the context of two examples.
The New AreaWide Model of the euro area: a microfounded openeconomy model for forecasting and policy analysis
, 2008
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Comparing Bootstrap and Posterior Probability Values in the FourTaxon Case
, 2003
"... Assessment of the reliability of a given phylogenetic hypothesis is an important step in phylogenetic analysis. Historically, the nonparametric bootstrap procedure has been the most frequently used method for assessing the support for specific phylogenetic relationships. The recent employment of Bay ..."
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Cited by 59 (4 self)
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Assessment of the reliability of a given phylogenetic hypothesis is an important step in phylogenetic analysis. Historically, the nonparametric bootstrap procedure has been the most frequently used method for assessing the support for specific phylogenetic relationships. The recent employment of Bayesian methods for phylogenetic inference problems has resulted in clade support being expressed in terms of posterior probabilities. We used simulated data and the fourtaxon case to explore the relationship between nonparametric bootstrap values (as inferred by maximum likelihood) and posterior probabilities (as inferred by Bayesian analysis). The results suggest a complex association between the two measures. Three general regions of tree space can be identified: (1) the neutral zone, where differences between mean bootstrap and mean posterior probability values are not significant, (2) near the twobranch corner, and (3) deep in the twobranch corner. In the last two regions, significant differences occur between mean bootstrap and mean posterior probability values. Whether bootstrap or posterior probability values are higher depends on the data in support of alternative topologies. Examination of star topologies revealed that both bootstrap and posterior probability values differ significantly from theoretical expectations;
K (2005) Polytomies and Bayesian phylogenetic inference. Syst Biol 54
"... 1 Abstract — Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There is, however, a growing number of examples in which large Bayesian posterior ..."
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Cited by 59 (0 self)
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1 Abstract — Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There is, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for nonBayesian measures of support such as nonparametric bootstrapping. For the fourtaxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible