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Smoothing and estimation methods in hidden variable models through sequential...
Hidden Markov chain models or more generally Feynman-Kac models are now widely used. They allow the modelling of a variety of time series (in finance, biology, signal... -
A mixed GM/SMC implementation of the probability hypothesis density filter
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Further Rao-Blackwellizing an already Rao-Blackwellized algorithm for jump Ma...
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Monte Carlo methods for sampling high-dimensional binary vectors
This thesis is concerned with Monte Carlo methods for sampling high-dimensional binary vectors from complex distributions of interest. If the state space is too large... -
Computational aspects of Bayesian spectral density estimation
Gaussian time-series models are often specified through their spectral density. Such models pose several computational challenges, in particular because of the... -
Advanced Interacting Sequential Monte Carlo Sampling for Inverse Scattering
The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from... -
A sequential Monte Carlo approach for MLE in a plant growth model
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On particle filters applied to electricity load forecasting
We are interested in the online prediction of the electricity load, within the Bayesian framework of dynamic models. We offer a review of sequential Monte Carlo... -
Initialize and Calibrate a Dynamic Stochastic Microsimulation Model : applica...
The purpose of this thesis is to develop statistical tools to initialize and to calibrate dynamic stochastic microsimulation models, starting from their application to... -
An island particle Markov chain Monte Carlo algorithm for safety analysis
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Optimal SIR algorithm vs. fully adapted auxiliary particle filter : a non asy...
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Estimation in the partially observed stochastic Morris-Lecar neuronal model w...
International audience
