Bayesian Nonparametrics (BNP) is a rapidly evolving area at the intersection of statistics, machine learning, probability and computer science. The focus is on modeling infinite-dimensional unknown objects that may consist of curves, surfaces, processes or probability measures. There are currently several vibrant communities focused on different aspects of BNP ranging from design of new processes motivated by machine learning and high-dimensional data problems, asymptotic properties, probabilistic properties, computation and intersections of these different areas. The area is amazingly multi-disciplinary, with leading researchers having diverse backgrounds in mathematics, statistics, probability theory, computer science and electrical engineering. The goal of this four-day workshop is to bring together a group of leading researchers having different perspectives on BNP including “outsiders” working on related areas relevant to BNP, such as optimization and probability, with the goal of spurring new collaborative projects aimed at developing transformative new approaches and high impact scientific tools.
The program followed and involved a subset of participants from the 10th Conference on Bayesian Nonparametrics, held in Raleigh, June 22-26, 2015. However, the summer program was a stand alone event, with its focus squarely placed on the synergy between mathematics, computer science, probability and statistics relevant to Bayesian Nonparametrics and involved its own set of leading researchers in the best interest of the cause. A primary aim of the program was to facilitate interaction between leading experts and graduate students and young researchers to help train the next generation BNP researchers.