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Ensemble MCMC sampling for robust Bayesian inference
Böhl, Gregor - 2022
macroeconomic models. It allows sampling from particularly challenging, high-dimensional black-box posterior distributions which may …
Persistent link: https://www.econbiz.de/10013473686
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Speeding up MCMC by efficient data subsampling
Quiroz, Matias; Villani, Mattias; Kohn, Robert - 2015
subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion … applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a …
Persistent link: https://www.econbiz.de/10010500806
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Evolutionary sequential Monte Carlo samplers for change-point models
Dufays, Arnaud - In: Econometrics : open access journal 4 (2016) 1, pp. 1-33
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...
Persistent link: https://www.econbiz.de/10011504888
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To bridge, to warp or to wrap? : a comparative study of Monte Carlo methods for efficient evaluation of marginal likelihoods
Ardia, David; Hoogerheide, Lennart; Dijk, Herman K. van - 2009
importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …
Persistent link: https://www.econbiz.de/10011377602
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To Bridge, to Warp or to Wrap? A Comperative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood
Ardia, David - 2015
use of importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …
Persistent link: https://www.econbiz.de/10012749869
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Fast, approximate MCMC for Bayesian analysis of large data sets : a design based approach
Kaeding, Matthias - 2016
. The subsample is taken via the cube method, a balanced sampling design, which is defined by the property that the sample … dass dieser nicht gespeichert werden muss. Die Stichprobe wird via cube sampling, einem balanciertem Stichprobendesign …
Persistent link: https://www.econbiz.de/10011566817
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On the Shape of Posterior Densities and Credible Sets in Instrumental Variable Regression Models with Reduced Rank : An Application of Flexible Sampling Methods Using Neural Networ...
Hoogerheide, Lennart F. - 2006
credible sets. When approximating such contours using Monte Carlo integration methods like importance sampling or Markov chain … numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate …
Persistent link: https://www.econbiz.de/10012734627
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Speeding up MCMC by delayed acceptance and data subsampling
Quiroz, Matias - 2015
observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling …
Persistent link: https://www.econbiz.de/10011300362
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A correction function approach to solve the incidental parameter problem
Li, Guangjie; Leon-Gonzalez, Roberto - 2009
Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....
Persistent link: https://www.econbiz.de/10003817215
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Adaptive polar sampling with an application to a Bayes measure of value-at-risk
Bauwens, Luc; Bos, Charles S.; Dijk, Herman K. van - 1999
Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill …
Persistent link: https://www.econbiz.de/10011302625