2007

• MCMC algorithms for distributions with intractable normalizing constants, with a view to perfect simulation and non-parametric Bayesian inference for inhomogeneous Markov point processes, DIMACS Workshop on Markov Chain Monte Carlo: Synthesizing Theory and Practice, The State University of New Jersey, Rutgers, USA.
• MCMC algorithms for distributions with intractable normalizing constants, with a view to perfect simulation and non-parametric Bayesian inference for inhomogeneous Markov point processes, Charles University, Prague.
• MCMC algorithms for distributions with intractable normalizing constants, with a view to perfect simulation and non-parametric Bayesian inference for inhomogeneous Markov point processes, Workshop on Point Process Modelling and Statistical Inference, Aalborg University.
• MCMC algorithms for distributions with intractable normalizing constants, with a view to perfect simulation and non-parametric Bayesian inference for inhomogeneous Markov point processes, Universidad Nacional Autónoma de Mexico, Mexico City.

2006

• Statistics and MCMC Computation of Voronoi Tessellations, The World a Jigsaw: Tessellations in the Sciences, Lorenz Center, Leiden, Netherlands.
• Permanental Point Processes, Sixth French-Danish Workshop on Spatial Statistics and Image Analysis in Biology, Skagen, Denmark.
• A Tutorial on Inference and Simulation for Spatial Point Processes, University of Aalborg, Denmark.
• Modern Statistics for Spatial Point Processes, 21st Nordic Conference on Mathematical Statistics, Rebild, Denmark.
• Modern Statistics for Spatial Point Processes, University of Toulouse 3, France.
• Modern Spatial Point Process Modelling and Inference, Conférences Plénières Sur les Modèles Spatiaux, Lille, France.
• Permanental Point Processes, Ludwig-Maximilians Universitët, Munich, Germany.

2005

• Perfect and approximate simulation of Hawkes processes, Ceremade - Université Paris-Dauphine, France.
• Dominating coupling from the past, University of Chicago, USA.
• Spatio-temporal models for red pine decline, Workshop on Recent Advances in Modelling Spatio-Temporal Data, Southampton, England.
• Residual analysis for spatial point processes, Royal Statistical Society, London, England.
• Spatio-temporal models for red pine decline, University of Abertey Dundee, Scotland.
• The permanental process, Conference on Stochastic Geometry and Its Applications, Lancaster University, England.
• The permanental process, Conference on Stochastic Geometry and Its Applications, University of Bern, Switzerland.
• Spatio-temporal models for red pine decline, Workshop on Bayesian Methods, Spatial Statistics and Ecology, Hannover, Germany.
• Perfect and approximate simulation of Hawkes processes, University of Western Australia, Australia.

2004

• Perfect simulation of complicated stochastic models: ideas and examples, Mathematical Days, University of Oulo, Finland.
• Perfect simulation of complicated stochastic models: ideas and examples, University of Rome 3, Italy.
• A Bayesian MCMC method for point process models with intractable normalising constants, International Conference on Spatial Point Process Modelling and Its Applications, Castellon.
• Inference for random tessellation models, Fifth French-Danish Workshop on Spatial Statistics and Image Analysis in Biology, Saint-Pierre de Chartreuse, France.
• Perfect and approximate simulation of Hawkes processes, Sixth World Congress of the Bernoulli Society for Mathematical Statistics and Probability, Barcelona, Spain.
• Inference for spatial point processes, 3e Cycle Romand de Stastisque et Probalitiés appliquées, Lausanne, Switzerland.
• Perfect and approximate simulation of Hawkes processes, University of Copenhagen, Denmark.