Workshop on Fusion Learning, BFF inferences and Statistical Foundations

April 11-13, 2016

Rutgers University, New Brunswick, NJ

The objective of this two-and-half day workshop is to study the role and foundation of statistical inference in the era of data science and also its applications in fusion learning. Its main foci are:

  • Report new advances in and re-examine the foundations of statistical inferences in the modern era of data science,
  • Develop links to bridge gaps among different statistical paradigms, including Bayesian, frequentist and fiducial (BFF) inferences, and explore the possibility of unifying statistical theme for scientific learning and research
  • Disseminate new ideas and foster new research approaches in fusion learning from multiple diverse data sources.

Professor Sir David R. Cox (Oxford University) is to deliver a featured address through a video presentation, to be discussed by Professor Nancy Reid (University of Toronto). Professors Jim Berger (Duke University) and Brad Efron (Stanford University)  are to deliver the keynote addresses, to be followed by many more distinguished lecturers and discussions. 

The workshop will bring together statisticians and data scientists across the aisles to address issues related to foundation of statistical inferences and its applications to combining information and fusion learning. This workshop will help disseminate new development of coherent BFF inferences and new advances in statistical inferences and their applications to both within the field of statistics and all fields that use statistics. It is sponsored by Rutgers Statistics Department, DIMACS center and the National Science Foundation (NSF).

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