**J. Olsson, J. Westerborn**,*Efficient particle-based online smoothing in general hidden Markov models: the PaRIS algorithm*. Submitted.**C. Vergé, P. Del Moral, E. Moulines, J. Olsson**,*Convergence properties of weighted particle islands with application to the double bootstrap algorithm*. Submitted.**C. Vergé, P. Del Moral, E. Moulines, J. Olsson**,*Supplement to "Convergence properties of weighted particle islands with application to the double bootstrap algorithm"*. Submitted.

**S. Bizjajeva, J. Olsson**(2015),*Antithetic sampling for sequential Monte Carlo methods with application to state space models*. To appear in the*Annals of the Institute of Statistical Mathematics*.**R. Douc, F. Maire, J. Olsson**(2015),*On the use of Markov chain Monte Carlo methods for the sampling of mixture models*.*Statistics and Computing*,**25:1**, pp. 95-110. (See also the introduction to our work given by S. Peluso.)**F. Maire, R. Douc, J. Olsson**(2014),*Comparison of asymptotic variances of inhomogeneous Markov chains with applications to Markov chain Monte Carlo methods*,*Annals of Statistics*,**42:4**, pp. 1483-1510. Direct access to the article.**R. Douc, E. Moulines, J. Olsson**(2014),*Long-term stability of sequential Monte Carlo methods under verifiable conditions*,*Annals of Applied Probability*,**24:5**, pp. 1767-1802. Direct access to the article.**J. Cornebise, E. Moulines, J. Olsson**(2014),*Adaptive sequential Monte Carlo by means of mixture of experts*,*Statistics and Computing*,**24:3**, pp. 317-337.**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.

**J. Olsson, J. Westerborn**(2015),*An efficient particle-based online EM algorithm for general state-space models*. To be presented at the*17th IFAC Symposium on System Identification (SYSID 2015)*, Beijing (China), October 2015.**C. Vergé, E. Moulines, J. Olsson**(2015),*Parallelization of sequential Monte Carlo methods using particle islands: the B*. To be presented at^{2}ASIL algorithm*The 2015 European Signal Processing Conference (EUSIPCO 2015)*, Nice (France), August 2015.**J. Westerborn, J. Olsson**(2014),*Efficient particle-based online smoothing in general hidden Markov models*. To be presented at the*2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)*, Florence (Italy), May 2014.

**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.

**J. Olsson**(2015),*Efficient particle-based smoothing in general hidden Markov models: the PaRIS algorithm*,*DYNSTOCH 2015*, Lund (Sweden), May 28, 2015. Slides of the talk.**J. Olsson**(2008),*Adaptive methods for sequential importance sampling*,*Journées MAS de la SMAI*, Rennes (France), August 2008. Slides of the talk.

**J. Olsson**(2014),*Comparison of asymptotic variance of inhomogeneous Markov chains with application to MCMC methods*, talk at the*16th Stockholm Uppsala Symposium in Mathematical Statistics*, Uppsala (Sweden), May 2014. Slides of the talk.**J. Olsson**(2014),*Particle islands and archipelagos: some large sample theory*, presented at the workshop*Advanced Monte Carlo Methods for Complex Inference Problems*at the Isaac Newton Institute for Mathematical Sciences, Cambridge (UK), April 2014.**J. Olsson**(2014),*Partial ordering of inhomogeneous Markov chains with applications to Markov Chain Monte Carlo methods*,*MCMski 4*, Chamonix (France), January 2014. Slides of the talk.**J. Olsson**(2013),*On the time uniform convergence of bootstrap-type particle filters*,*4th Linnaeus University Workshop in Stochastic Analysis and its Applications (LSAA)*, Växjö (Sweden), May 2013. Slides of the talk.**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.

**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.

**J. Olsson**(2015),*Efficient particle-based smoothing in general hidden Markov models: the PaRIS algorithm*, Seminar at*Uppsala University*,*Department of Information Technology*, Uppsala (Sweden), April 20, 2015. Slides of the talk.**J. Olsson**(2015),*Efficient particle-based smoothing in general hidden Markov models: the PaRIS algorithm*, Seminar at*Linköping University*,*Division of Automatic Control*, Linköping (Sweden), March 12, 2015. Slides of the talk.**J. Olsson**(2015),*Efficient particle-based smoothing in general hidden Markov models: the PaRIS algorithm*, Seminar at*Stockholm University*,*Department of Mathematics*, Stockholm (Sweden), February 11, 2015. Slides of the talk.**J. Olsson**(2014),*Comparison of asymptotic variances of inhomogeneous Markov chains with applications to Markov Chain Monte Carlo methods*, Seminar at*Linköping University*,*Department of Mathematics*, Linköping (Sweden), April 8, 2014. Slides of the talk.**J. Olsson**(2013),*Partial ordering of inhomogeneous Markov chains with applications to Markov chain Monte Carlo methods*,*Big'MC*seminar at*Institute Henri Poincaré*, Paris (France), November 21, 2013. Slides of the talk.**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.

**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.**J. Olsson, T. Rydén**(2004),*The bootstrap particle filtering bias*.*Preprints in Mathematical Sciences*,**2004:24**, Lund University.