Research and Publications

Preprints

R. Douc, E. Moulines, J. Olsson (2012), Long-term stability of sequential Monte Carlo methods under verifiable conditions. Preprints in Mathematical Sciences, 2012:1, Lund University. Submitted.
J. Olsson, T. Rydén, S. Stjernqvist (2010), A particle-based Markov chain Monte Carlo sampler for state-space models with applications to DNA copy number data. Preprints in Mathematical Sciences, 2010:12, Lund University. Submitted.
S. Bizjajeva, J. Olsson (2008), Antithetic sampling for sequential Monte Carlo methods with application to state space models. Preprints in Mathematical Sciences, 2008:14, Lund University. Submitted.
J. Olsson, T. Rydén (2004), The bootstrap particle filtering bias. Preprints in Mathematical Sciences, 2004:24, Lund University.

Journal papers

J. Cornebise, E. Moulines, J. Olsson (2012), Adaptive sequential Monte Carlo by means of mixture of experts. To appear in Statistics and Computing.
R. Douc, A. Garivier, E. Moulines, J. Olsson (2011), On the forward filtering backward smoothing particle approximations of the smoothing distribution in general state spaces models, Annals of Applied Probability, 21:6, pp. 2109-2145.
J. Olsson, T Rydén (2011), Rao-Blackwellisation of particle Markov chain Monte Carlo methods using forward filtering backward sampling, IEEE Transactions on Signal Processing 59:10, pp. 4606-4619.
J. Olsson, J. Ströjby (2011), Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators, Electronic Journal of Statistics 5, pp. 1090-1122. Open access.
R. Douc, E. Moulines, J. Olsson, R. van Handel (2011), Consistency of the Maximum Likelihood Estimator for general hidden Markov models, Annals of Statistics 39:1, pp. 474-513.
J. Cornebise, E. Moulines, J. Olsson (2008), Adaptive methods for sequential importance sampling with application to state space models, Statistics and Computing, 18:4, pp. 461-480. Direct access to the article.
R. Douc, É. Moulines, J. Olsson (2008), Optimality of the auxiliary particle filter, Probability and Mathematical Statistics, 29:1, pp. 1-28. Open access.
J. Olsson, T. Rydén (2008), Asymptotic properties of particle filter-based maximum likelihood estimators for state space models, Stochastic Processes and Their Applications,118, pp. 649-680.
J. Olsson, R, Douc, O. Cappé, É. Moulines (2008), Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models, Journal of the Bernoulli Society, 14:1, pp. 155-179. Direct access to the article.

Conference contributions

Conference papers (with peer-review)

R. Douc, E. Moulines, J. Olsson (2012), On the long-term stability of bootstrap-type particle filters. In Proceedings of the 16th IFAC Symposium on System Identification (SYSID), Brussels (Belgium), July 2012.
F. Lindsten, T. Schön, J. Olsson (2011), An explicit variance reduction expression for the Rao-Blackwellized particle filter. Presented at the 18th World Congress of the International Federation of Automatic Control (IFAC), Milano (Italy), Aug 2011.
S. Hägnesten, J. Olsson (2010), Option-based maximum likelihood estimation in stochastic volatility models. Presented at the 6th World Congress of the Bachelier Finance Society, Toronto (Canada), June 2010.
J. Olsson, J. Ströjby (2010), Approximation of hidden Markov models by mixtures of experts with application to particle filtering. Presented at the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Chia Laguna Resort, Sardinia (Italy), May 2010. In JMLR Workshop and Conference Proceedings, 9, pp. 573-580. Direct access to the article.
J. Olsson, J. Ströjby (2009), Efficient particle-based likelihood estimation in partially observed diffusion processes. In Proceedings of the 15th IFAC Symposium on System Identification (SYSID), Saint-Malo (France), July 2009.
R. Douc, A. Garivier, É. Moulines, J. Olsson (2009), Approximation particulaire par FFBS de la loi de lissage pour des HMM dans des espaces d'états généraux. Presented at the 41èmes Journées de Statistique, Bordeaux (France), May 2009.
J. Cornebise, É. Moulines, J. Olsson (2008), Adaptive methods for sequential importance sampling with application to state space models. In Proceedings of the 16th European Signal Processing Conference (EUSIPCO), Lausanne (Switzerland), August 2008.
J. Cornebise, É. Moulines, J. Olsson (2008), Adaptive methods for sequential importance sampling with application to state space models. In Proceedings of the Fourth International Workshop on Applied Probability (IWAP), Compiègne (France), July 2008. Slides of the talk given by J. Cornebise.
R. Douc, É. Moulines, J. Olsson (2007), Improving the performance of the two-stage sampling particle filter: a statistical perspective. In Proceedings of IEEE/SP 14th Workshop on Statistical Signal Processing, Madison (USA), August 2007, pp. 284-288. Direct access to the article and poster of the presentation given by myself.
J. Olsson, R, Douc, O. Cappé, É. Moulines (2007), On the use of sequential Monte Carlo methods for approximating smoothing functionals, with application to fixed parameter estimation, Workshop Sequential Monte Carlo Methods: filtering and other applications, Oxford (UK), July 2006. In Conference Oxford sur les méthodes de Monte Carlo séquentielles (C. Andrieu and D. Crisan eds.), ESAIM Proceedings, 19, pp. 6-11. Direct access to the article and Slides of the talk given by O. Cappé.
J. Olsson, T. Rydén (2007), Particle filter-based approximate maximum likelihood inference asymptotics in state-space models, Workshop Sequential Monte Carlo Methods: filtering and other applications, Oxford (UK), July 2006. In Conference Oxford sur les méthodes de Monte Carlo séquentielles (C. Andrieu and D. Crisan eds.), ESAIM Proceedings, 19, pp. 115-120. Direct access to the article.

Contributed conference talks

J. Olsson (2008), Adaptive methods for sequential importance sampling, Journées MAS de la SMAI, Rennes (France), August 2008. Slides of the talk.

Invited conference talks

J. Olsson (2012), On the long-term stability of bootstrap-type particle filters, 16th IFAC Symposium on System Identification (SYSID), Brussels (Belgium), July 2012. Slides of the talk. See also the conference paper above.
J. Olsson (2012), Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators, 1st Conference of the International Society for NonParametric Statistics (ISNPS), Chalkidiki (Greece), June 2012. Slides of the talk.
J. Olsson (2012), Consistency of the maximum likelihood estimator for general hidden Markov models, 24th Nordic Conference in Mathematical statistics (Nordstat), Umeå (Sweden), June 2012. Slides of the talk.

Theses

J. Olsson (2006), On Bounds and Asymptotics of Sequential Monte Carlo Methods for Filtering, Smoothing, and Maximum Likelihood Estimation in State Space models, Doctoral Thesis in Mathematical Sciences 2006:13, Lund University. The thesis can be downloaded here.
J. Olsson (2005), On Estimation in State Space Models Using the Bootstrap Particle Filter, Licentiate Thesis in Mathematical Sciences 2005:6, Lund University.
J. Olsson (2002), Computational Methods for Lévy Driven Russian Options, Master's Thesis in Mathematical Sciences 2002:E26, Lund University.

Invited seminars and talks

J. Olsson (2012), Metropolising forward particle filtering backward simulation and Rao-Blackwellisation using multiple trajectories, Seminar at Chalmers Institute of Technology, Department of Mathematical Sciences, Göteborg (Sweden), December 11, 2012. Slides of the talk.
J. Olsson (2012), Metropolising forward particle filtering backward simulation and Rao-Blackwellisation using multiple trajectories, Seminar at TELECOM SudParis, Département Communications, Images et Traitement de l'Information, Paris (France), November 8, 2012. Slides of the talk.
J. Olsson (2012), Long-term stability of sequential Monte Carlo methods under verifiable conditions, Talk at the Statistics Meeting Lund University and University of Copenhagen, University of Copenhagen, Department of Mathematical Sciences, Copenhagen (Denmark), April 11, 2012. Slides of the talk.
J. Olsson (2011), Smoothing in general hidden Markov models using sequential Monte Carlo methods, Seminar at Lund University, Department of Statistics, April 27, 2011. Slides of the talk.
J. Olsson (2010), Smoothing in general hidden Markov models using sequential Monte Carlo methods, Seminar at University of Copenhagen, Department of Mathematical Sciences, Copenhagen (Denmark), December 3, 2010. Slides of the talk.
J. Olsson (2010), Smoothing in general hidden Markov models using sequential Monte Carlo methods, Seminar at KTH Royal Institute of Technology, Division of Mathematical Statistics, Stockholm (Sweden), September 27, 2010. Slides of the talk.
J. Olsson (2010), Smoothing in general hidden Markov models using sequential Monte Carlo methods, Seminar at Linköping University, Division of Automatic Control, Linköping (Sweden), February 11, 2010. Slides of the talk.
J. Olsson (2007), On particle-based estimation in state space models, Séminaire: Méthodes de Monte Carlo Adaptatives, Institut Henri Poincaré, Paris (France), March 29, 2007. Slides of the talk.
J. Olsson (2005), Asymptotic properties of the bootstrap particle filter maximum likelihood estimator for state space models, Seminar at TELECOM ParisTech, Département TSI, Paris (France), October 20, 2005.

Last modified: 2012/07/14, 11:38 GMT