Convolution particle filters for parameter estimation in general state-space models

When sequential data assimilation is considered, the joint distribution of the latent state and the observations is described mathematically using a state-space model, and filtering

Abstract—In the context of Distributed Source Coding, we propose a low complexity algorithm for the estimation of the cross-over probability p of the Binary Symmetric Channel

Background & Objective: In this work, we focus on estimating the parameters of the widely used Gompertz tumor growth model, based on measurements of the tumor’s volume.. Being

Index Terms— Hidden Markov model, parameter estimation, particle filter, convolution kernels, conditional least squares estimate, maximum likelihood estimate.. Suppose that the

Abstract: This paper deals with parameter and state estimation in a bounded-error context for uncertain dynamical aerospace models when the input is considered optimized or not.. In

The predictive perfor- mances of the two methods are evaluated by an application to the LNAS sugar beet growth model with three sets of real measurements, one used for

Key-words: Hidden Markov models, parameter estimation, particle filter, convolution kernels, conditional least squares estimate, maximum likelihood estimate9. (Résumé

In particular, a numerical illustration is given for the three following models: a Gaussian AR(1) model for which our approach can be well understood, an AR(1) process with