165 citations · 521 across the 31 of their papers we have counts for
12 papers · 1 filter
Adaptive online variance estimation in particle filters: the ALVar estimator
Alessandro Mastrototaro, Jimmy Olsson
We present a new approach-the ALVar estimator-to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The method, which adjusts adaptively the…
Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm
Randal Douc, Alain Durmus, Aurélien Enfroy +1
The present paper focuses on the problem of sampling from a given target distribution defined on some general state space. To this end, we introduce a novel class of non-revers…
Fast and numerically stable particle-based online additive smoothing: the AdaSmooth algorithm
Alessandro Mastrototaro, Jimmy Olsson, Johan Alenlöv
We present a novel sequential Monte Carlo approach to online smoothing of additive functionals in a very general class of path-space models. Hitherto, the solutions proposed in the…
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.…
Particle-based adaptive-lag online marginal smoothing in general state-space models
Johan Alenlöv, Jimmy Olsson
We present a novel algorithm, an adaptive-lag smoother, approximating efficiently, in an online fashion, sequences of expectations under the marginal smoothing distributions in gen…
Particle-based, online estimation of tangent filters with application to parameter estimation in nonlinear state-space models
Jimmy Olsson, Johan Westerborn Alenlöv
This paper presents a novel algorithm for efficient online estimation of the filter derivatives in general hidden Markov models. The algorithm, which has a linear computational com…