Date: 2015-02-20

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Rapidly progressing particle tracking techniques have revealed that foreign particles in biological fluids exhibit rich and at times unexpected behavior, with important consequences for disease diagnosis and drug delivery. Yet, there remains a frustrating lack of coherence in the description of these particles’ motion. Largely this is due to a reliance on functional statistics (e.g., mean-squared displacement) to perform model selection and assess goodness-of-fit. However, not only are such functional characteristics typically estimated with substantial variability, but also they may fail to distinguish between a number of stochastic processes — each making fundamentally different predictions for relevant quantities of scientific interest. In this talk, I will describe a detailed Bayesian analysis of leading candidate models for subdiffusive particle trajectories in human pulmonary mucus. Efficient and scalable computational strategies will be proposed. Model selection will be achieved by way of intrinsic Bayes factors, which avoid both non-informative priors and “using the data twice”. Goodness-of-fit will be evaluated via second-order criteria along with exact model residuals. Our findings suggest that a simple model of fractional Brownian motion describes the data just as well as a first-principles physical model of visco-elastic subdiffusion.

Speaker

Martin Lysy is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo.