Luis Nieto Barajas (ITAM - Instituto Tecnologico Autonomo de Mexico)
A Bayesian nonparametric approach for time series clustering
In this work we propose a model-based clustering method for time series. The model uses an almost surely discrete Bayesian nonparametric prior to induce clustering of the series. Specifically we propose a general Poisson-Dirichlet process mixture model, which includes the Dirichlet process mixture model as particular case. The model accounts for typical features present in a time series like trends, seasonal and temporal components. All or only part of these features can be used for clustering according to the user. Posterior inference is obtained via an easy to implement Markov chain Monte Carlo (MCMC) scheme. The best cluster is chosen according to a heterogeneity measure as well as the model selection criteria LPML (logarithm of the pseudo marginal likelihood). We illustrate our approach with a dataset of time series of shares prices in the Mexican stock exchange.

David Draper (University of California, Santa Cruz, USA, and Ebay Research Labs, San Jose, California, USA)
Why the bootstrap works; and when and why log scores are a good way to compare Bayesian models
In this talk I'll describe recent work on two unrelated topics:
(a) How the frequentist bootstrap may be understood as an approximate Bayesian non-parametric method, which explains why the bootstrap works and when it doesn't, and
(b) Why log scores are a good way to compare Bayesian models, and when they're better than Bayes factors at doing so.