Adaptive methods for sequential importance sampling with application to state space models. - Julien Cornebise; Eric Moulines; Jimmy Olsson (Stat. Comput., 2008)

Approximation of hidden Markov models by mixtures of experts with application to particle filtering. - Jimmy Olsson; Jonas Ströjby (AISTATS, 2010)

Efficient particle-based online smoothing in general hidden Markov models. - Johan Westerborn; Jimmy Olsson (ICASSP, 2014)

Adaptive sequential Monte Carlo by means of mixture of experts. - Julien Cornebise; Eric Moulines; Jimmy Olsson (Stat. Comput., 2014)

On the use of Markov chain Monte Carlo methods for the sampling of mixture models: a statistical perspective. - Randal Douc; Florian Maire; Jimmy Olsson (Stat. Comput., 2015)

Efficient parameter inference in general hidden Markov models using the filter derivatives. - Jimmy Olsson; Johan Westerborn (ICASSP, 2016)

Sequential sampling of junction trees for decomposable graphs. - Jimmy Olsson; Tetyana Pavlenko; Felix L. Rios (CoRR, 2018)

Particle-Based Adaptive-Lag Online Marginal Smoothing in General State-Space Models. - Johan Alenlöv; Jimmy Olsson (IEEE Trans. Signal Process., 2019)

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. - Gabriel Cardoso 001; Sergey Samsonov; Achille Thin; Eric Moulines; Jimmy Olsson (NeurIPS, 2022)

Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm. - Randal Douc; Alain Durmus; Aurélien Enfroy; Jimmy Olsson (CoRR, 2022)

Sequential sampling of junction trees for decomposable graphs. - Jimmy Olsson; Tatjana Pavlenko; Felix L. Rios (Stat. Comput., 2022)

State and parameter learning with PARIS particle Gibbs. - Gabriel Cardoso 001; Yazid Janati El Idrissi; Sylvain Le Corff; Eric Moulines; Jimmy Olsson (ICML, 2023)

Backward Importance Sampling for Online Estimation of State Space Models. - Alice Martin 001; Marie-Pierre Étienne; Pierre Gloaguen; Sylvain Le Corff; Jimmy Olsson (J. Comput. Graph. Stat., 2023)

Adaptive online variance estimation in particle filters: the ALVar estimator. - Alessandro Mastrototaro; Jimmy Olsson (Stat. Comput., 2023)

A similarity-based Bayesian mixture-of-experts model. - Tianfang Zhang; Rasmus Bokrantz; Jimmy Olsson (Stat. Comput., 2023)

Online Variational Sequential Monte Carlo. - Alessandro Mastrototaro; Jimmy Olsson (ICML, 2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors. - Yazid Janati; Badr Moufad; Alain Durmus; Eric Moulines; Jimmy Olsson (NeurIPS, 2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. - Yazid Janati El Idrissi; Alain Durmus; Eric Moulines; Jimmy Olsson (CoRR, 2024)

Recursive Learning of Asymptotic Variational Objectives. - Alessandro Mastrototaro; Mathias Müller; Jimmy Olsson (AISTATS, 2025)

Variational Diffusion Posterior Sampling with Midpoint Guidance. - Badr Moufad; Yazid Janati; Lisa Bedin; Alain Oliviero Durmus; Randal Douc; Eric Moulines; Jimmy Olsson (ICLR, 2025)

A Mixture-Based Framework for Guiding Diffusion Models. - Yazid Janati; Badr Moufad; Mehdi Abou El Qassime; Alain Durmus; Eric Moulines; Jimmy Olsson (CoRR, 2025)

Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance. - Badr Moufad; Yazid Janati; Alain Durmus; Ahmed Ghorbel; Eric Moulines; Jimmy Olsson (CoRR, 2025)