Jimmy Olsson

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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 resampling 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)
A prevalent problem in general state-space models is the approximation of the smoothing distribution of a state, or a sequence of states, conditional on the observations from the past, the present, and the future. The aim of this paper is to provide a rigorous foundation for the calculation, or approximation, of such smoothed distributions, and to analyse(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 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)
The junction tree representation provides an attractive structural property for organizing a decomposable graph. In this study, we present a novel stochastic algorithm which we call the Christmas tree algorithm for building of junction trees sequentially by adding one node at a time to the underlying decomposable graph. The algorithm has two important(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 multiplier weights are seldom known. In this paper we present a simulation-based method for constructing(More)
In this paper we study asymptotic properties of weighted samples produced by the two-stage sampling (TSS) particle filter, which is a generalization of the auxiliary particle filter proposed by [1]. Besides establishing a central limit theorem (CLT) for the particle estimator of the smoothing measure, we also present bounds on the Lp error and bias of the(More)
In this study we present a sequential sampling methodology for Bayesian inference in decomposable graphical models. We recast the problem of graph estimation, which in general lacks natural sequential interpretation, into a sequential setting. Specifically, we propose a recursive Feynman-Kac model which generates a flow of junction tree distributions over a(More)