Efficient Learning via Simulation: A Marginalized Resample-Move Approach

Fülöp, András; Li, Junye

doi:10.2139/ssrn.1724203

Cited by 26 publications

(36 citation statements)

References 36 publications

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“…We believe that the TNT algorithm could be adapted to recent SMC algorithms such as [12,13] since they propose advanced SMC samplers based on the IBIS and the E-AIS samplers. Another avenue of research could be an application on change-point stochastic volatility models.…”

Section: Discussionmentioning

confidence: 99%

“…By doing so, we come back to the framework with static parameter space. For non linear state space model, recent works of [13,14] rely on the particle MCMC framework of [34] for integrating out the state vector. We believe that switching from the tempered domain to the time one as well as employing the evolutionary MCMC kernel presented above could even more increase the efficiency of these sophisticated SMC samplers.…”

Section: Adaptation Of the Scalementioning

confidence: 99%

“…We believe that switching from the tempered domain to the time one as well as employing the evolutionary MCMC kernel presented above could even more increase the efficiency of these sophisticated SMC samplers. For example, the particle discrepancies of the early stage inherent to the IBIS algorithm is present in all the empirical simulations of [13] whereas with the TNT sampler, we can ensure a minimum ESS value during the entire procedure.…”

Section: Adaptation Of the Scalementioning

confidence: 99%

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Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Dufays

2016

Econometrics

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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 algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in this paper, combines (off-line) tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time.

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Section: Discussionmentioning

confidence: 99%

Section: Adaptation Of the Scalementioning

confidence: 99%

Section: Adaptation Of the Scalementioning

confidence: 99%

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Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Dufays

2016

Econometrics

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“…The reader is referred to Chopin et al (2013) for further details including a formal justification (see also Fulop and Li (2013) for a related algorithm and Jacob (2015) for a recent discussion). Recall the target posterior at time t, p(c|y 1:t ) given by (3).…”

Section: Smc 2 Schemementioning

confidence: 99%

Efficient $$\hbox {SMC}^2$$ SMC 2 schemes for stochastic kinetic models

Golightly

Kypraios

2017

Stat Comput

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Fitting stochastic kinetic models represented by Markov jump processes within the Bayesian paradigm is complicated by the intractability of the observed-data likelihood. There has therefore been considerable attention given to the design of pseudo-marginal Markov chain Monte Carlo algorithms for such models. However, these methods are typically computationally intensive, often require careful tuning and must be restarted from scratch upon receipt of new observations. Sequential Monte Carlo (SMC) methods on the other hand aim to efficiently reuse posterior samples at each time point. Despite their appeal, applying SMC schemes in scenarios with both dynamic states and static parameters is made difficult by the problem of particle degeneracy. A principled approach for overcoming this problem is to move each parameter particle through a Metropolis-Hastings kernel that leaves the target invariant. This rejuvenation step is key to a recently proposed SMC 2 algorithm, which can be seen as the pseudo-marginal analogue of an idealised scheme known as iterated batch importance sampling. Computing the parameter weights in SMC 2 requires running a particle filter over dynamic states to unbiasedly estimate the intractable observed-data likelihood up to the current time point. In this paper, we propose to use an auxiliary particle filter inside the SMC 2 scheme. Our method uses two recently proposed constructs for sampling conditioned jump processes, and we find that the resulting inference schemes typically require fewer state particles than when using a simple bootstrap filter. Using two applications, we compare the performance B Andrew Golightly

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“…We also propose various methodological extensions. Related ideas have been proposed more recently in scenarios where a static parameter, θ, is the object inferred by the top level algorithm, rather than the Markov process {X n } n≥1 [Chopin et al, 2011, Fulop andLi, 2011]. …”

Section: Introductionmentioning

confidence: 99%

Exact Approximation of Rao-Blackwellised Particle Filters

Johansen

Whiteley

Doucet

2012

IFAC Proceedings Volumes

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Particle methods are a category of Monte Carlo algorithms that have become popular for performing inference in non-linear non-Gaussian state-space models. The class of "RaoBlackwellised" particle filters exploits the analytic marginalisation that is possible for some statespace models to reduce the variance of the Monte Carlo estimates. Despite being applicable to only a restricted class of state-space models, such as conditionally linear Gaussian models, these algorithms have found numerous applications. In scenarios where no such analytical integration is possible, it has recently been proposed in Chen et al. [2011] to use "local" particle filters to carry out this integration numerically. We propose here an alternative approach also relying on "local" particle filters which is more broadly applicable and has attractive theoretical properties. Proof-of-concept simulation results are presented.

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Efficient Learning via Simulation: A Marginalized Resample-Move Approach

Cited by 26 publications

References 36 publications

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

Efficient $$\hbox {SMC}^2$$ SMC 2 schemes for stochastic kinetic models

Exact Approximation of Rao-Blackwellised Particle Filters

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