Bayesian data analysis continues to be increasingly important for a wide range of fields in the physical, social, and medical sciences. The Bayesian Statistics (BS) team is concerned with all methodologies of Bayesian data analysis. The methodologies broadly encompass parametric (finite-dimensional) Bayesian models, nonparametric (infinite-dimensional) Bayesian models, approaches to empirically test and compare the adequacy of such models, as well as studies of asymptotic and other theoretical aspects of Bayesian models.
The methodologies also encompass all computational methods for estimating the posterior distributions of Bayesian models, including Markov chain Monte Carlo methods, sequential Monte Carlo methods, and methods for estimating Bayesian models from large-scale data sets. Also, we encourage the development of new user-friendly statistical software packages, for a wide range of Bayesian models.