Sara Wade joined the group as a Research Associate (Postdoc) in November 2012. She received a B.Sc. in Mathematics at the University of Maryland, College Park. In January 2013, she earned her PhD in Statistics from Bocconi University in Milan in January 2013, where she worked on Bayesian nonparametric regression with Professor Sonia Petrone.
Her research interests include regression; density estimation; conditional density estimation; mixture models; clustering; feature allocation; (dependent) random measures; Markov Chain Monte Carlo methods; and Bayesian nonparametrics, machine learning, and statistics in general.
- Antoniano Villalobos I., Wade S., and Walker S. G. (2013). “A Bayesian nonparametric regression model with normalized weights; A study of hippocampal atrophy in Alzheimer’s disease.” Submitted.
- Wade S., Dunson D., Petrone S., Trippa, L. (2013). “Improving prediction from Dirichlet process mixtures via enrichment.” Submitted. pdf
- Wade S., Mongelluzzo S., and Petrone S. (2011). “A enriched conjugate prior for Bayesian nonparametric inference.” Bayesian Analysis 6: 359-386. pdf
- Wade S., Walker S. G., and Petrone S. (2013). “A predictive study of Dirichlet process mixture models for curve fitting.” To appear in the Scandinavian Journal of Statistics. DOI: 10.1111/sjos.12047. link