Density-Tempered Marginalized Sequential Monte Carlo Samplers

Duan, Jin‐Chuan; Fülöp, András

doi:10.1080/07350015.2014.940081

Cited by 48 publications

(66 citation statements)

References 22 publications

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“…The IS and SMC approaches have been extended to allow for unbiased likelihood estimators to be used (see Chopin et al (2013), Tran et al (2014), Duan and Fulop (2015) and Drovandi and McCutchan (2016)). The IS and SMC methods are of additional interest as they produce also an estimate of the evidence, which can be used for fully Bayesian model comparisons; see, for example, Drovandi and McCutchan (2016) and Carson et al (2017).…”

Section: Discussionmentioning

confidence: 99%

Accelerating pseudo-marginal MCMC using Gaussian processes

Drovandi

Moores

Boys

2018

Computational Statistics & Data Analysis

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The grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms are pseudo-marginal methods used to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but tends to give conservative approximations of the posterior and is still expensive. A new method is developed to accelerate the GIMH method by using a Gaussian process (GP) approximation to the log-likelihood and train this GP using a short pilot run of the MCWM algorithm. This new method called GP-GIMH is illustrated on simulated data from a stochastic volatility and a gene network model. The new approach produces reasonable posterior approximations in these examples with at least an order of magnitude improvement in computing time. Code to implement the method for the gene network example can be found at http://www.runmycode.org/companion/view/2663.

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Section: Discussionmentioning

confidence: 99%

Accelerating pseudo-marginal MCMC using Gaussian processes

Drovandi

Moores

Boys

2018

Computational Statistics & Data Analysis

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“…Of course, in the models considered in this work, p(y|θ) is intractable and is therefore again approximated using a PF. This idea was first employed by Duan and Fulop (2015) and it shares some similarities with the SMC 2 approach from Chopin et al (2013) which we discuss at the end of this section. Algorithm 4 outlines the SMC sampler; we use the convention that any action specified for the mth particle is to be performed conditionally independently for all m ∈ {1, .…”

Section: Smc Sampler For Evidence Approximationmentioning

confidence: 99%

Efficient Sequential Monte Carlo Algorithms for Integrated Population Models

Finke

King

Beskos

et al. 2019

JABES

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State-space models are commonly used to describe different forms of ecological data. We consider the case of count data with observation errors. For such data the system process is typically multi-dimensional consisting of coupled Markov processes, where each component corresponds to a different characterisation of the population, such as age group, gender or breeding status. The associated system process equations describe the biological mechanisms under which the system evolves over time. However, there is often limited information in the count data alone to sensibly estimate demographic parameters of interest, so these are often combined with additional ecological observations leading to an integrated data analysis. Unfortunately, fitting these models to the data can be challenging, especially if the state-space model for the count data is non-linear or non-Gaussian. We propose an efficient particle Markov chain Monte Carlo algorithm to estimate the demographic parameters without the need for resorting to linear or Gaussian approximations. In particular, we exploit the integrated model structure to enhance the efficiency of the algorithm. We then incorporate the algorithm into a sequential Monte Carlo sampler in order to perform model comparison with regards to the dependence structure of the demographic parameters. Finally, we demonstrate the applicability and computational efficiency of our algorithms on two real datasets.

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“…This method is conceptually close to the method proposed by Duan and Fülöp (2013). In a similar set-up, they suggest to integrate out the states using a particle filter.…”

Section: Non-gaussian Transition Densitymentioning

confidence: 99%

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

et al. 2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents inSiem Jan Koopman, (a,c), ‡ André Lucas A particular feature of our approach is that smoothed estimates of the states and the marginal likelihood are obtained directly as an output of the algorithm. Our method provides a computationally efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the performance of our method for stock returns and corporate default panel data.JEL classification: C11, C15, C22, C32, C58.

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Density-Tempered Marginalized Sequential Monte Carlo Samplers

Cited by 48 publications

References 22 publications

Accelerating pseudo-marginal MCMC using Gaussian processes

Accelerating pseudo-marginal MCMC using Gaussian processes

Efficient Sequential Monte Carlo Algorithms for Integrated Population Models

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

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