International audienceThe situations where particle filtering fails (so-called weight degeneracy) can be detected with the asymptotic variance of the particle approximation. However, this asymptotic variance is in general intractable, and in the case of weight degeneracy, computing it by Monte Carlo sampling is inefficient. We propose to compute the asymptotic variance of the particle approximation via the Laplace method for multidimensional integrals. We present this method, and illustrate how it can be used to improve particle filtering robustness
ii We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the fi...
Abstract — In recent work it is shown that importance sampling can be avoided in the particle filter...
The problem of degeneracy in marginalized particle filtering is addressed. In particular, we note tha...
La thèse porte sur l'apport de la méthode de Laplace pour l'approximation du filtre bayésien dans de...
This paper concerns numerical assessment of Monte Carlo error in particle filters. We show that by k...
International audienceThe deterministic Laplace method is combined with particle filtering for the s...
The thesis deals with the contribution of the Laplace method to the approximation of the Bayesian fi...
We introduce a weighted particle representation for the solution of the filtering problem based on a...
Abstract. In this article we study asymptotic properties of weighted samples produced by the auxilia...
In many applications, a state-space model depends on a parameter which needs to be inferred from dat...
26 pagesIn this article we study asymptotic properties of weighted samples produced by the auxiliary...
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computation...
Particle filters are a popular and flexible class of numerical algorithms to solve a large class of ...
In this paper we present on-line Bayesian filtering methods for non-linear multivariate time series ...
In this paper we present on-line Bayesian filtering methods for time series models corrupted by asym...
ii We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the fi...
Abstract — In recent work it is shown that importance sampling can be avoided in the particle filter...
The problem of degeneracy in marginalized particle filtering is addressed. In particular, we note tha...
La thèse porte sur l'apport de la méthode de Laplace pour l'approximation du filtre bayésien dans de...
This paper concerns numerical assessment of Monte Carlo error in particle filters. We show that by k...
International audienceThe deterministic Laplace method is combined with particle filtering for the s...
The thesis deals with the contribution of the Laplace method to the approximation of the Bayesian fi...
We introduce a weighted particle representation for the solution of the filtering problem based on a...
Abstract. In this article we study asymptotic properties of weighted samples produced by the auxilia...
In many applications, a state-space model depends on a parameter which needs to be inferred from dat...
26 pagesIn this article we study asymptotic properties of weighted samples produced by the auxiliary...
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computation...
Particle filters are a popular and flexible class of numerical algorithms to solve a large class of ...
In this paper we present on-line Bayesian filtering methods for non-linear multivariate time series ...
In this paper we present on-line Bayesian filtering methods for time series models corrupted by asym...
ii We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the fi...
Abstract — In recent work it is shown that importance sampling can be avoided in the particle filter...
The problem of degeneracy in marginalized particle filtering is addressed. In particular, we note tha...