The tutorial covers cross-validation, and projection predictive approaches for model assessment, selection and inference after model selection and Bayesian stacking for model averaging. The talk is ...

This course will give a gentle introduction to SMC (Sequential Monte Carlo algorithms):

• motivation: state-space (hidden Markov) models, sequential analysis of such models; non-sequential problems ...

This talk focuses on the estimation of the distribution of unobserved nodes in large random graphs from the observation of very few edges. These graphs naturally model tournaments involving a large ...

This is a short introduction to the many directions of current research in Bayesian computational statistics, from accelerating MCMC algorithms, to using partly deterministic Markov processes like ...