The field of Bayesian nonparametrics has seen a remarkable growth over the last 15 years. It is concerned with placing priors on infinite dimensional objects such as unknown distributions (which may depend on covariates) and leads to flexible models and powerful estimation procedures. The methods have been used for problems such as density estimation, survival analysis, clustering, factor analysis and semiparametric modelling - often combined with Bayesian hierarchical modelling and approximate computational methods. This has lead to many applications in areas such as biology, economics, finance, medicine and machine learning.
This track is concerned with all areas of Bayesian semiparametric and nonparametric modelling, computation and application.
Full papers containing a strong computational or data analytic component will be considered for publication in a Special Issue, or regular issue of the journal Computational Statistics and Data Analysis. All submissions must contain original unpublished work not being considered for publication elsewhere. Submissions will be refereed according to standard procedures for CSDA.
