141 citations · 177 across the 8 of their papers we have counts for
8 papers
Adaptive importance sampling for heavy-tailed distributions via -divergence minimization
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux +1
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy ta…
Graphs in State-Space Models for Granger Causality in Climate Science
Víctor Elvira, Émilie Chouzenoux, Jordi Cerdà +1
Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from ano…
Differentiable Bootstrap Particle Filters for Regime-Switching Models
Wenhan Li, Xiongjie Chen, Wenwu Wang +2
Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models. In real-world app…
Variance Analysis of Multiple Importance Sampling Schemes
Rahul Mukerjee, Víctor Elvira
Multiple importance sampling (MIS) is an increasingly used methodology where several proposal densities are used to approximate integrals, generally involving target probability de…
Large Data and (Not Even Very) Complex Ecological Models: When Worlds Collide
Ruth King, Blanca Sarzo, Víctor Elvira
We consider the challenges that arise when fitting complex ecological models to 'large' data sets. In particular, we focus on random effect models which are commonly used to descri…
Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility
Luca Martino, Jesse Read, Victor Elvira +1
We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modal…