Nonparametric Learning from Bayesian Models
Speaker: Chris Holmes (Strategic Programme Director for Health- Alan Turing Institute)
Date: 3rd July 2018
Bayesian learning is built on an assumption that the model space contains a true reflection of the data generating mechanism. In reality all models are false. If the data is simple and small, and the models are sufficiently rich, then the consequences of model misspecification may not be severe. Increasingly however data is being captured at scale, both in terms of the number of observations as well as the diversity of data modalities. This is particularly true of modern biomedical applications, where analysts are faced with integration of medical images, genetics, genomics, and biomarker measurements. If Bayesian inference is to remain at the forefront of data-science then we will need new theory and computational methods that accommodate the approximate nature of scalable models.
In this talk I will discuss recent research in the field of Bayesian nonparametrics with fast updating schemes and approximate models motivated by large-scale health applications involving multivariate measurements on 100,000s of subjects.
Chris Holmes is Professor of Biostatistics at the University of Oxford with a joint Chair between the Departments of Statistics and the Nuffield Department of Medicine. He holds a Programme Leaders award from the Medical Research Council (MRC), and is Scientific Director for Health at the Alan Turing Institute and the HDR UK.