Importance sampling for online variational learning - Département Image, Données, Signal Access content directly
Preprints, Working Papers, ... Year : 2024

Importance sampling for online variational learning

Abstract

This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling. We propose an efficient algorithm for computing the gradient of the evidence lower bound (ELBO) in the context of streaming data, where observations arrive sequentially. Our contributions include a computationally efficient online ELBO estimator, demonstrated performance in offline and true online settings, and adaptability for computing general expectations under joint smoothing distributions.
Fichier principal
Vignette du fichier
22-onlinevariational_hal.pdf (511.39 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04435065 , version 1 (02-02-2024)

Identifiers

Cite

Mathis Chagneux, Pierre Gloaguen, Sylvain Le Corff, Jimmy Olsson. Importance sampling for online variational learning. 2024. ⟨hal-04435065⟩
25 View
5 Download

Altmetric

Share

Gmail Facebook X LinkedIn More