Speaker: Ioannis Sgouralis

Affiliation: University of Tennessee Knoxville

Title: Data models and analysis with Bayesian nonparametric methods.

Abstract: Experiments nowadays monitor physical systems with high resolution that routinely reaches the molecular level. Excessive noise stemming from the measuring devises and the protocols followed or unaccounted processes demand the formulation of specialized methods for the analysis and interpretation of the datasets acquired. Nevertheless, physical limitations and the inherent uncertainties in the underlying systems, such as unknown characteristics and behavior, which translate to unknown parameters, states, or dynamics, pose unique conceptual and computational challenges that lead to intractable problems of model selection. In this talk, I will present an overview of the difficulties that are commonly encountered especially with biochemical data. I will also highlight recent advances, including Bayesian approaches to data modeling and non-parametric statistical learning, which provide feasible alternatives to model selection.