Identification of jump Markov linear models using particle filters. - Andreas Svensson; Thomas B. Schön; Fredrik Lindsten (CDC, 2014)

Marginalizing Gaussian process hyperparameters using sequential Monte Carlo. - Andreas Svensson; Johan Dahlin; Thomas B. Schön (CAMSAP, 2015)

Nonlinear state space model identification using a regularized basis function expansion. - Andreas Svensson; Thomas B. Schön; Arno Solin; Simo Särkkä (CAMSAP, 2015)

Nonlinear state space smoothing using the conditional particle filter. - Andreas Svensson; Thomas B. Schön; Manon Kok (CoRR, 2015)

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models. - Andreas Svensson; Arno Solin; Simo Särkkä; Thomas B. Schön (AISTATS, 2016)

A flexible state-space model for learning nonlinear dynamical systems. - Andreas Svensson; Thomas B. Schön (Autom., 2017)

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. - Thomas B. Schön; Andreas Svensson; Lawrence M. Murray; Fredrik Lindsten (CoRR, 2017)

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. - Andreas Svensson; Fredrik Lindsten; Thomas B. Schön (CoRR, 2017)

How consistent is my model with the data? Information-Theoretic Model Check. - Andreas Svensson; Dave Zachariah; Thomas B. Schön (CoRR, 2017)

Ontology-Based Knowledge Representation for Increased Skill Reusability in Industrial Robots. - Elin Anna Topp; Maj Stenmark; Alexander Ganslandt; Andreas Svensson; Mathias Haage; Jacek Malec (IROS, 2018)

Learning dynamical systems with particle stochastic approximation EM. - Andreas Svensson; Fredrik Lindsten (CoRR, 2018)

New AIR for the Archaeological Process? The Use of 3D Web Semantic for Publishing Archaeological Reports. - Paola Derudas; Federico Nurra; Andreas Svensson (ACM Journal on Computing and Cultural Heritage, 2023)