STA 290 Seminar: Robin Gong

Robin Gong

Event Date

Mathematical Sciences 1147 (Colloquium Room)

SPEAKER: Robin Gong, Assistant Professor, Statistics, Rutgers University

TITLE : “Modeling uncertainty with sets of probabilities”

ABSTRACT: In statistical modeling, uncertainty cannot always be faithfully captured by a single probability distribution. The modeler can be unsure how to specify a prior for a Bayesian model, or to assume a probabilistic mechanism for the missing data, or to quantify uncertainty for an under-identified model parameter.  In this talk, I motivate sets of probabilities as an attractive modeling strategy, which encodes low-resolution information in both the data and the model spaces with little need to concoct unwarranted assumptions. I present a Dempster-Shafer model for multinomial parameter estimation, which delivers essentially prior-free posterior inference and can be understood as versions of differentially private Bayesian histogram estimation. I discuss challenges that arise with the employment of belief and capacity functions, special cases of sets of probabilities, and how the choice of conditioning rules reconciles among a trio of unsettling posterior phenomena: dilation, contraction and sure loss. These findings underscores the invaluable role of judicious judgment in handling low-resolution probabilistic information.

Speaker's web page:


DATE:                    Thursday, January 24th, 4:10pm

LOCATION:          MSB 1147, Colloquium Room

REFRESHMENTS: 3:30pm MSB 4110 (4th floor lounge)

STA 290 Seminar List: