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On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers
Dufays, Arnaud - 2014
slightly different distributions. The method encompasses the Iterated Batch Importance Sampling (IBIS) algorithm and more …
Persistent link: https://www.econbiz.de/10011588382
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On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers
Dufays, Arnaud - 2014
slightly different distributions. The method encompasses the Iterated Batch Importance Sampling (IBIS) algorithm and more …
Persistent link: https://www.econbiz.de/10011506783
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Evolutionary sequential Monte Carlo samplers for change-point models
Dufays, Arnaud - In: Econometrics 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/10011755319
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Evolutionary Sequential Monte Carlo Samplers for Change-Point Models
Dufays, Arnaud - 2018
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. Nevertheless 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 SMC...
Persistent link: https://www.econbiz.de/10012936969
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On the Conjugacy of Off-Line and On-Line Sequential Monte Carlo Samplers
Dufays, Arnaud - 2014
slightly different distributions. The method encompasses the Iterated Batch Importance Sampling (IBIS) algorithm and more …
Persistent link: https://www.econbiz.de/10013047483
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On the conjugacy of off-line and on-line Sequential Monte Carlo samplers
Dufays, Arnaud - 2014
Persistent link: https://www.econbiz.de/10010416851
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Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models
Barra, Istvan - 2016
these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method …
Persistent link: https://www.econbiz.de/10013005987
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Marginal Likelihood for Markov-Switching and Change-Point GARCH Models
Bauwens, Luc - 2011
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved...
Persistent link: https://www.econbiz.de/10013118012
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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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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