DMS Statistics and Data Science Seminar

IN PERSON

Speaker:  Ioannis Sgouralis (University of Tennessee)

Title: Bayesian nonparametric modeling of biophysical and biochemical data

Abstract: Modern experiments monitor physical systems with high resolution that may reach the molecular level. Excessive noise stemming from the measuring hardware and the experimental procedures or unaccounted processes demand the formulation of specialized methods for the analysis of the datasets acquired. Nevertheless, physical limitations and the inherent uncertainties in the underlying systems, such as unknown parameters, states, or dynamics pose unique conceptual and computational challenges that, under physically realistic data representations, lead to intractable problems of model selection. In this talk, I will present an overview of the difficulties that are commonly encountered especially with single molecule measurements. I will also highlight recent advances, including Bayesian non-parametric approaches, which provide feasible alternatives to model selection.