7 citations · 8 across the 8 of their papers we have counts for
8 papers
Efficient Online Variational Estimation via Monte Carlo Sampling
Mathis Chagneux, Mathias Müller, Pierre Gloaguen +2
This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradien…
Importance sampling for online variational learning
Mathis Chagneux, Pierre Gloaguen, Sylvain Le Corff +1
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…
Amortized backward variational inference in nonlinear state-space models
Mathis Chagneux, Élisabeth Gassiat, Pierre Gloaguen +1
We consider the problem of state estimation in general state-space models using variational inference. For a generic variational family defined using the same backward decompositio…
Backward importance sampling for online estimation of state space models
Alice Martin, Marie-Pierre Etienne, Pierre Gloaguen +2
This paper proposes a new Sequential Monte Carlo algorithm to perform online estimation in the context of state space models when either the transition density of the latent state…
A pseudo-marginal sequential Monte Carlo online smoothing algorithm
Pierre Gloaguen, Sylvain Le Corff, Jimmy Olsson
We consider online computation of expectations of additive state functionals under general path probability measures proportional to products of unnormalised transition densities.…
The Langevin diffusion as a continuous-time model of animal movement and habitat selection
Théo Michelot, Marie-Pierre Etienne, Pierre Gloaguen
1. The utilisation distribution describes the relative probability of use of a spatial unit by an animal. It is natural to think of it as the long-term consequence of the animal's…
Online Sequential Monte Carlo smoother for partially observed stochastic differential equations
Pierre Gloaguen, Marie-Pierre Etienne, Sylvain Le Corff
This paper introduces a new algorithm to approximate smoothed additive functionals for partially observed stochastic differential equations. This method relies on a recent procedur…
Stochastic differential equation based on a multimodal potential to model movement data in ecology
Pierre Gloaguen, Marie-Pierre Etienne, Sylvain Le Corff
This paper proposes a new model for individuals movement in ecology. The movement process is defined as a solution to a stochastic differential equation whose drift is the gradient…