Bayesian, Fiducial, and Frequentist (BFF) Conferences

** Deadline for applications for this workshop is March 20, 2019 **

Applications received after March 20th are subject to availability.

Location

This workshop will be held at Penn Pavilion on the campus of Duke University.

Description

The Bayesian, Fiducial, and Frequentist (BFF) conferences began in 2014 as a means of facilitating scientific exchange among statisticians developing new BFF methodologies. These conferences offer an opportunity to examine different statistical paradigms, and compare various methodologies. This year BFF6 is being held jointly with the 2018-2019 Model Uncertainty: Mathematical and Statistical (MUMS) program at the Statistical and Applied Mathematical Sciences Institute (SAMSI), and in particular MUMS working groups on the Foundations of Model Uncertainty and on Data Fusion.

** Notice of Consent **

SAMSI values the proprietary and intellectual property of our participants. The materials presented at our various workshops and programs are in high demand by event participants and the applied mathematics and statistics community that comprise our audience. Therefore, we encourage all of our invited speakers to share their materials, as appropriate, in order to pass along the valuable research that is being done in your field of study and is a focus of this event. In addition, unless SAMSI is give written approval from our speakers we ARE NOT authorized to share the materials presented at this event.

Please click HERE to complete a SAMSI Consent form for this event. SAMSI appreciates your time and willingness to share this valuable content with others and we hope you enjoy this event!
For any questions or concerns about our consent policy, please contact us at: communications@samsi.info

** A short course will be presented on April 28; research contributions will be presented April 29-May 1. **.

Schedule and Supporting Media

Printed Schedule
Titles and Abstracts
Poster Titles

Sunday, April 28, 2019
Penn Pavilion, Duke University, Durham, NC

Time Description Speaker Slides
8:30am Registration
8:50-9:00am Welcome and Introductory Information
9:00am-12:00pm Short Course on Bayesian Model Uncertainty: Model Uncertainty: A Review Anabel Forte, University of Valencia
10:30-10:45am BREAK
12:00-2:00pm LUNCH on own
2:00-5:15pm Short Course on Data Fusion
Fusion Learning and BFF Approaches Regine Liu, Rutgers University
Min-ge Xie, Rutgers University
3:30-3:45pm BREAK
5:15pm Shuttle to Hotel

Monday, April 29, 2019
Penn Pavilion, Duke University, Durham, NC

Time Description Speaker Slides
8:45-9:00am Opening Remarks David Banks, SAMSI
Ruobin Gong, Rutgers University
9:00-10:00am Keynote Lecture: Inference Meets Computation: Dynamical, Stochastic and Economic Perspectives Michael Jordan, University of California, Berkeley
10:00-10:15am BREAK
10:15-11:45am Invited Session: BFF Interfaces
Blends of Bayesian and Frequentist Inference David Bickel, University of Ottawa
Multidimensional Monotonicity Discovery with MBART Ed George, University of Pennsylvania
Calibration of Probability Forecasts Vladimir Vovk, Royal Holloway, University of London
11:45am-1:15pm LUNCH on own
1:15-2:15pm Keynote Lecture: Statistical Sparsity Peter McCullagh, University of Chicago
2:15-2:30pm BREAK
2:30-4:00pm Invited Session: Uncertainty Quantification for Bayesian Nonparametrics
Multiscale Analysis of BART Veronika Rockova, University of Chicago
Uncertainty Quantification for Bayesian Survival Analysis Stéphanie van der Pas, Leiden University
Coverage of Credible Intervals for Monotone Regression Subhashis Ghosal, N.C. State University
4:00-4:30pm BREAK
4:30-5:15pm Panel Session Moderator: Nancy Reid, University of Toronto Statistics
Panelists Glenn Shafer, Rutgers Statistics/Business School
Peter Song, Michigan Biostatistics
Naveen Narisetty, UIUC Statistics
Ruobin Gong, Rutgers Statistics
5:30-7:00pm Poster Session and Reception
7:00-8:00pm Public Lecture: Discussing his new book “AIQ: How People and Machines Are Smarter Together” James Scott, University of Texas, Austin
8:20pm Shuttle to Hotel

Tuesday, April 30, 2019
Penn Pavilion, Duke University, Durham, NC

Time Description Speaker Slides
9:00-10:00am Keynote Lecture: Can a Fiducial Phoenix Rise from the Ashes? Phil Dawid, Cambridge University
10:00-10:15am BREAK
10:15-11:45am Invited Session: SAMSI Developments on Data Fusion
Generalized Probabilistic Principal Component Analysis of Correlated Data Mengyang Gu, Johns Hopkins University
Are Reported Likelihood Ratios Well Calibrated? Jan Hannig, University of North Carolina, Chapel Hill
Bayesian Analysis for Misaligned Regions and Applications in Cancer Mortality Dongchu Sun, University of Missouri
11:45am-1:15pm LUNCH on own
1:15-2:15pm Keynote Lecture: Spatially Informed Variable Selection Priors and Applications to Large-scale Data Marina Vannucci, Rice University
2:15-2:30pm BREAK
2:30-4:00pm Invited Session: SAMSI Developments in Model Uncertainty
Variable Selection in the Discrepancy Function Associated with a Simulator Pierre Barbillon, SAMSI and UMR MIA-Paris, AgroParisTech, INRA
Including Factors in Bayesian Variable Selection Problems Gonzalo Garcia Donato, Universidad de Castilla-La Mancha
Model Selection in the Context of Computer Models Rui Paulo, ISEG, Technical University of Lisbon
4:00-4:30pm BREAK
4:30-5:30pm Student Invited Session
Objective Bayesian Analysis for a 2 x 2 Contingency Table John Snyder, Bayer Crop Science
Inference on Treatment Effects after Model Selection Jingshen Wang, University of Michigan
The EAS Approach for Graphical Selection Consistency in Vector Autoregression Models Jonathan Williams, University of North Carolina, Chapel Hill
5:45pm Shuttle to Hotel

Wednesday, May 1, 2019
Penn Pavilion, Duke University, Durham, NC

 

Time Description Speaker Slides
9:00-10:00am Keynote Lecture: Selecting Important Features in Presence of Correlation—a story from Genetics Chiara Sabatti, Stanford University
10:00-10:15am BREAK
10:15-11:45am Invited Session: Philosophical Perspective on Model Uncertainty
A New, Truth-directed Explanation of Ockham’s Razor in Model Inference Kevin Kelly, Carnegie Mellon University
Unifying Ockham’s Razor Leah Henderson, University of Groningen
Models as Tools not Mirrors: Crossover Themes from the Philosophy of Science Alisa Bokulich, Boston University
11:45am-12:00pm Final Discussion
12:00-12:10pm Closing Remarks Ruobin Gong, Rutgers University
Jan Hannig, University of North Carolina, Chapel Hill
12:30pm  → Shuttle to Airport

Questions: email mums@samsi.info