On selecting the hyperparameters of the DPM models for the density estimation of observation errors - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

On selecting the hyperparameters of the DPM models for the density estimation of observation errors

Résumé

The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied especially when the distribution of latent parameters is to be considered as multimodal. DPMs allow for uncertainty in the choice of parametric forms and in the number of mixing components (clusters). The parameters of a DP include the precision a and the base probability measure G0(μ, Σ). In most applications, the choice of priors and posteriors computation for the hyperparameters (α, μ, Σ) clearly influences inferences about the level of clustering in the mixture. This is the main focus of this paper. We consider the problem of density estimation of an observation noise distribution in a dynamic nonlinear model from a Bayesian nonparametric view-point. Our approach is illustrated in a real-world data analysis task dealing with the estimation of pseudorange errors in a GNSS based localization context.
Fichier non déposé

Dates et versions

hal-00712723 , version 1 (27-06-2012)

Identifiants

Citer

Asma Rabaoui, Emmanuel Duflos, Juliette Marais, Nicolas Viandier. On selecting the hyperparameters of the DPM models for the density estimation of observation errors. International Conference on Acoustic, Speech and Signal Processing (ICASSP°, May 2011, Prague, Czech Republic. pp.4092-4095, ⟨10.1109/ICASSP.2011.5947252⟩. ⟨hal-00712723⟩
472 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More