Growing up, Merlise Clyde loved science, math, and the outdoors but really had no idea about careers in mathematics, let alone statistics. She discovered statistics while at Oregon State University studying forestry ecology. There was no major in statistics then, but she took every statistics course she could fit into her degree. Clyde ultimately earned her PhD in statistics from the University of Minnesota. She is best known for her research in Bayesian model selection/model averaging with mixtures of g-priors (and the associated R package BAS); however, her greatest accomplishment has been in Bayesian nonparametric regression using Lévy processes for Lévy adaptive regression kernels.
Amstat News
The Membership Magazine of the American Statistical Association