Andriy Andreev: Statistical Inference with Enlarged Filtration: a Bayesian Data Augmentation Analysis

Complex biological systems are almost always only partially observable and standard statistical modeling, based solely on observed data, can sometimes result in misleading conclusions about relationships of interest. Data augmentation has in its various forms proved to be useful by adding the unobservable part to the likelihood. This talk will present a research evolution of one non-parametrically defined hierarchical intensity model. Predictive distributions are used to access causal influences, while MCMC disentangles conditioning on interdependent random processes.