165 citations · 521 across the 31 of their papers we have counts for
33 papers
State and parameter learning with PaRIS particle Gibbs
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff +2
Non-linear state-space models, also known as general hidden Markov models, are ubiquitous in statistical machine learning, being the most classical generative models for serial dat…
Particle-based, rapid incremental smoother meets particle Gibbs
Gabriel Cardoso, Eric Moulines, Jimmy Olsson
The particle-based, rapid incremental smoother (PARIS) is a sequential Monte Carlo technique allowing for efficient online approximation of expectations of additive functionals und…
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…
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Gabriel Cardoso, Sergey Samsonov, Achille Thin +2
Importance Sampling (IS) is a method for approximating expectations under a target distribution using independent samples from a proposal distribution and the associated importance…
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…
Probabilistic Pareto plan generation for semiautomated multicriteria radiation therapy treatment planning
Tianfang Zhang, Rasmus Bokrantz, Jimmy Olsson
Objective: We propose a semiautomatic pipeline for radiation therapy treatment planning, combining ideas from machine learning-automated planning and multicriteria optimization (MC…