On Monday, August 26, 2019, Prof. Boris Houska (ShanghaiTech University, China) gave a lecture on "Moment Based Learning".
Abstract: The first part of this talk presents a moment based approach for supervised statistical learning with applications in system identification and control. For this aim, we introduce a novel class of generating functions for analyzing the moments of the posterior distribution of Bayesian updates. These functions enable us to develop computational algorithms that can learn general nonlinear models from streaming data by maintaining a sequence of moments via Bayesian inference and generalized unscented propagation without ever approximating the underlying probability distributions directly.
A second part of the talk introduces a novel open-source software,
named MBL-Toolbox, which implements a generic tool for moment
based learning (MBL) and which is scheduled to be released
publicly in the near future. We provide a crash course on how to
use our current beta-version of this new tool discussing a number
of tutorial problems for Bayesian learning and nonlinear dynamic
system identification. Here, we focus on developing an intuition
of what the advantages and disadvantages of the Bayesian viewpoint
are compared to traditional least-squares based parameter
estimation techniques, as, for example, used in modern moving
horizon estimators for nonlinear systems.