Nonparametric Bayesian Inference - Computational Issues

Statistical models are almost never right. All models involve certain parametric and structural assumptions. Bayesian nonparametric inference (BNP) is an increasingly widely used approach to mitigate the dependence on such assumptions. After a rapid expansion of BNP research over the past 30 years, BNP is now a mature research area in statistics and machine learning. The current main challenges are related to computational hurdles and bottlenecks and the closely related need to tackle more complex and highly structured problems. This program bring together researchers working in BNP, including computation, foundations, methodology and application of BNP methods, with the goal of identifying newly emerging computational strategies and inference approaches. The program and invited talks are planned to balance theoretical expertise, interest and prowess in computational methods, and exposure to selected substantial application areas. The intended nature of the program as identifying synergies of different approaches and potentially new research directions naturally leads to favoring breath over depth, with more emphasis on covering diverse areas rather than on in-depth discussions of a single specific theme.