1 citations · 1 across the 9 of their papers we have counts for
6 papers · 1 filter
A forward-only scheme for online learning of proposal distributions in particle filters
Sylvain Procope-Mamert, Nicolas Chopin, Maud Delattre +1
We introduce a new online approach for constructing proposal distributions in particle filters using a forward scheme. Our method progressively incorporates future observations to…
Extrapolation of Tempered Posteriors
Mengxin Xi, Zheyang Shen, Marina Riabiz +2
Tempering is a popular tool in Bayesian computation, being used to transform a posterior distribution into a reference distribution that is more easily approximated. Se…
Least squares variational inference
Yvann Le Fay, Nicolas Chopin, Simon Barthelmé
Variational inference consists in finding the best approximation of a target distribution within a certain family, where `best' means (typically) smallest Kullback-Leiber divergenc…
Saddlepoint Monte Carlo and its Application to Exact Ecological Inference
Théo Voldoire, Nicolas Chopin, Guillaume Rateau +1
Assuming X is a random vector and A a non-invertible matrix, one sometimes need to perform inference while only having access to samples of Y = AX. The corresponding likelihood is…
A connection between Tempering and Entropic Mirror Descent
Nicolas Chopin, Francesca R. Crucinio, Anna Korba
This paper explores the connections between tempering (for Sequential Monte Carlo; SMC) and entropic mirror descent to sample from a target probability distribution whose unnormali…
Computational aspects of Bayesian spectral density estimation
Nicolas Chopin, Judith Rousseau, Brunero Liseo
Gaussian time-series models are often specified through their spectral density. Such models present several computational challenges, in particular because of the non-sparse nature…