Approximate Uncertainty Quantification with Jump Processes and Applications

by Iker Perez
Monday 15th October 11:00, 2018

In this talk, Iker Perez, Assistant Professor in Statistics, School of Mathematical Sciences and Horizon Digital Economy Research, University of Nottingham will discuss foundational statistical challenges and probabilistic considerations associated with families of stochastic jump models, which often find applications in technological domains such as network analysis or operations research. He will review Markov jump processes, and by means of common accessible examples, discuss the strong impediments posed by real-world application scenarios to inverse uncertainty quantification tasks.

Next, he will discuss current statistical advances linked to structured jump systems along with relevant literature. Through a model exemplar borrowed from queueing theory, he will finally present an approximate and scalable variational Bayesian framework, suitable for uncertainty quantification tasks with a large class of structured processes. The talk will further include examples with applications of the methods introduced.