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- François Le Gland, Laurent Mevel
- MCSS
- 2000

We consider a hidden Markov model with multidimensional observations, and with misspecification, i.e. the assumed coefficients (transition probability matrix, and observation conditional densities)… (More)

We present in this article a genetic type interacting particle systems algorithm and a genealogical model for estimating a class of rare events arising in physics and network analysis. We represent… (More)

The ensemble Kalman lter (EnKF) has been proposed as a Monte Carlo, derivative free, alternative to the extended Kalman lter, and is now widely used in sequential data assimilation, where state… (More)

We consider an hidden Markov model with multidimensional observations, and with misspecii-cation, i.e. the assumed coeecients (transition probability matrix, and observation conditional densities)… (More)

- Paul Bui Quang, Christian Musso, François Le Gland
- 2012 Proceedings of the 20th European Signal…
- 2012

The Laplace method and Monte Carlo methods are techniques to approximate integrals which are useful in nonlinear Bayesian computation. When the model is one-dimensional, Laplace formulas to compute… (More)

- François Le Gland, Laurent Mevel
- MCSS
- 2000

We use basic properties of the projective product, to obtain exponential bounds for the Lipschitz constant associated with the projective product of column{allowable nonnegative matrices. We obtain… (More)

- Pierre Blanchart, Liyun He, François Le Gland
- 2009 12th International Conference on Information…
- 2009

This paper describes a fusion approach to the problem of indoor localization of a pedestrian user, in which PNS measurements, cartographic constraints and ranging or proximity beacon measurements are… (More)

Monte Carlo simulations are a classical tool to analyse physical systems. When unlikely events are to be simulated, the importance sampling technique is often used instead of Monte Carlo. Importance… (More)

In this paper, the problem of identifying a hidden M arkov model (HMM) with general state space, e.g. a partially observed diffusion pr cess, is considered. A particle implementation of the recursive… (More)

- Paul Bui Quang, Christian Musso, François Le Gland
- 2010 13th International Conference on Information…
- 2010

Particle filtering is a widely used Monte Carlo method to approximate the posterior density in non-linear filtering. Unlike the Kalman filter, the particle filter deals with non-linearity,… (More)