The award recognizes outstanding work in Bayesian analysis, Pearce’s specialty.
By Bennett Campbell Ferguson
October 6, 2026
Professor Michael Pearce [statistics], working in collaboration with Elena A. Erosheva, has won the Mitchell Prize, which is awarded in recognition of an outstanding paper that describes how Bayesian analysis has solved an important applied problem.
“It’s an honor to have my work recognized,” Pearce says. “The award is meant to recognize Bayesian approaches to solving really important or pressing problems in the social sciences—and I definitely see that as the driver of my work.”
Pearce and Erosheva (who is Pearce’s dissertation adviser and a professor of statistics at the University of Washington) received the award for their paper “Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings,” published in the Journal of the American Statistical Association.
Unlike the statisticians who adhere to the more common frequentist approach, Bayesians utilize both current data and data from past studies. Pearce has done much to encourage this approach at Reed, where he created a course on Bayesian statistics.
“In this work, we’re dealing with the social science problem of how to make decisions—especially in important, grant-funding settings,” he says. “I hope that this award means that more people will see this work and think about the fact that the way it’s always been done is not always the best way to keep going.”