Location
This workshop was 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 was 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 **
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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: [email protected]
Keynote Lecture
Date/Location: April 29, 2019, 7-8pm at the Penn Pavilion on the campus of Duke University
Title: How People and Machines are Smarter Together
Description:Our world today is filled with complex machines and technologies that have the capability to learn and adapt. From smart phones to driverless cars, these machines understand how humans interact with the world by using the wealth of data made available to them through our interactions every day. This technology is helping to reshape our future the same way the Industrial Revolution remade the 19th century.
In the book AIQ: How People and Machines are Smarter Together, James Scott and Nick Polson address how mathematics drives the language spoken by the artificially intelligent machines in our world today. The book takes us through history and teaches us about data, probability and better thinking. In addition, the book shows how ideas from the past are being used to help intelligent machines to adapt in the modern age using big data. Understanding these technologies can help us to overcome some of our cognitive shortcomings and help us to lead happier and more productive lives.
In this lecture, James Scott talked about his new book and discusses the concepts addressed in it.
** 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
Keynote Lecture Flyer
Participant List
Sunday, April 28, 2019
Penn Pavilion, Duke University, Durham, NC
| Description | Speaker | Slides |
|---|---|---|
| Registration | ||
| Short Course on Bayesian Model Uncertainty: Model Uncertainty: A Review | Anabel Forte, University of Valencia | |
| Short Course on Data Fusion | ||
| Fusion Learning and BFF Approaches | Regine Liu, Rutgers University | |
| Min-ge Xie, Rutgers University |
Monday, April 29, 2019
Penn Pavilion, Duke University, Durham, NC
| Description | Speaker | Slides |
|---|---|---|
| Welcome and Introductory Information | Cynthia Rudin, Duke University | |
| Keynote Lecture: Inference Meets Computation: Dynamical, Stochastic and Economic Perspectives | Michael Jordan, University of California, Berkeley | |
| Invited Session: BFF Interfaces | ||
| Maximum Entropy Derived and Generalized Under Idempotent Probability to Address Bayes-Frequentist Uncertainty and Model Revision Uncertainty | 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 | |
| Keynote Lecture: Statistical Sparsity | Peter McCullagh, University of Chicago | |
| 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 | |
| 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 | ||
| Poster Session and Reception | ||
| Public Lecture: Discussing his new book “AIQ: How People and Machines Are Smarter Together” | James Scott, University of Texas, Austin |
Tuesday, April 30, 2019
Penn Pavilion, Duke University, Durham, NC
| Description | Speaker | Slides |
|---|---|---|
| Keynote Lecture: Can a Fiducial Phoenix Rise from the Ashes? | Phil Dawid, Cambridge University | |
| 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 | |
| Keynote Lecture: Spatially Informed Variable Selection Priors and Applications to Large-scale Data | Marina Vannucci, Rice University | |
| 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 | |
| Student Invited Session | ||
| Objective Bayesian Analysis for a 2 x 2 Contingency Table | John Snyder, University of Missouri | |
| 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 |
Wednesday, May 1, 2019
Penn Pavilion, Duke University, Durham, NC
| Description | Speaker | Slides |
|---|---|---|
| Keynote Lecture: Selecting Important Features in Presence of Correlation—a story from Genetics | Chiara Sabatti, Stanford University | |
| 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 | |
| Final Discussion | ||
| Closing Remarks | Ruobin Gong, Rutgers University | |
| Jan Hannig, University of North Carolina, Chapel Hill |
Questions: email [email protected]