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This paper concerns the use of Sequential Monte Carlo methods (SMC) for smoothing in general state space models. A well known problem when applying the standard SMC technique in the smoothing mode is that the resam-pling mechanism introduces degeneracy of the approximation in the path-space. However, when performing maximum likelihood estimation via the EM(More)
In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms - also known as particle filters-relying on new criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used(More)
Dans les espaces d'´ etat généraux, il est souvent complexe de construire une bonne approximation de la loi de lissage d'unétat, ou d'une suite d'´ etats, conditionnellement aux observations passées, présentes et futures. L'objet de cette contribution est de fournir un cadre rigoureux pour le calcul ou l'approximation de telles distributions de lissage, et(More)
Selecting appropriately the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm approximating adaptively the so-called optimal proposal kernel by a mixture of integrated(More)
This paper deals with the problem of estimating expectations of sums of additive functionals under the joint smoothing distribution in general hidden Markov models. Computing such expectations is a key ingredient in any kind of expectation-maximization-based parameter inference in models of this sort. The paper presents a computationally efficient algorithm(More)
In this paper we study asymptotic properties of different data-augmentation-type Markov chain Monte Carlo algorithms sampling from mixture models comprising discrete as well as continuous random variables. Of particular interest to us is the situation where sampling from the conditional distribution of the continuous component given the discrete component(More)
Selecting conveniently the proposal kernel and the adjustment multiplier weights of the auxiliary particle filter may increase significantly the accuracy and computational efficiency of the method. However, in practice the optimal proposal kernel and multi-plier weights are seldom known. In this paper we present a simulation-based method for constructing(More)
Estimating online the parameters of general state-space hidden Markov models is a topic of importance in many scientific and engineering disciplines. In this paper we present an online parameter estimation algorithm obtained by casting our recently proposed particle-based, rapid incremental smoother (PaRIS) into the framework of recursive maximum likelihood(More)
This thesis consists of two papers studying online inference in general hidden Markov models using sequential Monte Carlo methods. The first paper present an novel algorithm, the particle-based, rapid in-cremental smoother (PaRIS), aimed at efficiently perform online approximation of smoothed expectations of additive state functionals in general hidden(More)
In this note we apply the particle-based iterated filtering algorithm proposed by Ionides et al. (2009) to the problem of calibration of stochastic volatility models. We assume that the underlying asset follows the Heston stochastic volatility dynamics and consider a hidden Markov model formulation of the observed option prices where the volatility of the(More)