The Bayesian, Fiducial, and Frequentist (BFF) community began in 2014 as a means to facilitate scientific exchange among statisticians and scholars in related fields that develop new methodologies with in mind the foundational principles of statistical inference. The community encourages and promotes research activities to bridge foundations for statistical inferences, to facilitate objective and replicable scientific learning, and to develop analytic and computing methodologies for data analysis.
The community collectively organizes an annual BFF conference series (past conferences). These conferences have served as an opportunity for statisticians, philosophers, computer scientists and mathematicians with shared interests to come together and examine different inferential paradigms, discuss novel methodologies and computation, and embark on a discourse on the principles and practices of inference under uncertainty.
- BFF7: October 26 – 28, 2020, Toronto, Canada – Registration is Open !
- Workshop on BFF Paradigm in Data Integration, Machine Learning and Applications – Nov 2, 2019
- BFF Handbook (forthcoming)
- Williams, J. P., & Hannig, J. (2019). Nonpenalized variable selection in high-dimensional linear model settings via generalized fiducial inference. The Annals of Statistics, 47(3), 1723-1753.
- Shen, J., Liu, R. Y., & Xie, M. G. (2019). iFusion: Individualized Fusion Learning. Journal of the American Statistical Association, (in press), 1-49.
- Fraser, D. A. S., Reid, N., & Lin, W. (2018). When should modes of inference disagree? Some simple but challenging examples. The Annals of Applied Statistics, 12(2), 750-770.
- Reid, N., & Cox, D. R. (2015). On some principles of statistical inference. International Statistical Review, 83.2: 293-308.