Sara Wade

Sara Wade

I have moved to the University of Warwick! Please visit: Dr. Sara Wade

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. Applications of interest include the prediction and assessment of Alzheimer’s disease based on neuroimaging data.




  • Prestia, A., Caroli, A., Wade, S., van der Flier, W.M., Ossenkoppele, R., Van Berckel, B., Barkhof, F., Teunissen C.E., Wall, A., Carter, S.F., Scholl, M., Choo, I.H., Nordberg, A., Scheltens, P., and Frisoni, G.B. (2015). “Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics”. Alzheimer’s & Dementia. link
  • Caroli, A., Prestia, A., Wade, S., Chen, K., Ayutyanont, N., Landau, S.M., Madison, C.M., Haense, C., Herholz, K., Reiman, E.M., Jagust, W.J., and Frisoni, G.B. (2014). “Alzheimer’s disease biomarkers as outcome measures for clinical trials in MCI”. Alzheimer’s Disease and Associated Disorders. link
  • Antoniano Villalobos I., Wade S., and Walker S. G. (2014). “A Bayesian nonparametric regression model with normalized weights; A study of hippocampal atrophy in Alzheimer’s disease.” Journal of the American Statistical Association, 109:477-490. link
  • Wade S., Dunson D., Petrone S., Trippa, L. (2014). “Improving prediction from Dirichlet process mixtures via enrichment.” Journal of Machine Learning Research, 15:1041-1071. link
  • Wade S., Walker S. G., and Petrone S. (2014). “A predictive study of Dirichlet process mixture models for curve fitting.” Scandinavian Journal of Statistics, 41:580-605. link
  • Wade S., Mongelluzzo S., and Petrone S. (2011). “A enriched conjugate prior for Bayesian nonparametric inference.” Bayesian Analysis, 6:359-386. link

Submitted Papers:

  • Wade, S. and Ghahramani, Z. (2015). Bayesian cluster analysis: Point estimation and credible balls. arXiv:1505.03339.


  • R package mcclust.ext (2015) for point estimation and credible balls to summarize Bayesian clustering models. mcclust.ext_1.0.tar.gz; Manual.

PhD Thesis: “Bayesian nonparametric regression through mixture models” (2013). pdf