One School Seminar: Cui -- From Matrix Interpolation to Tensorized High-Dimensional Random Variable Simulation with Applications to Bayesian Computation

Abstract: Simulating intractable high-dimensional random variables is one of the
fundamental challenges in stochastic computation.  This task has broad applications in
statistical physics, machine learning, uncertainty quantification, econometrics, and
beyond.  In this talk, we will present how to formulate such tasks as function
approximation problems and solve them using tensor computation.  We will also show how
the tensor computation problem can be reduced to simple recursive matrix
interpolations.  As a result, we obtain algorithms with complexity linear in the problem
dimension.  We will demonstrate the efficiency and efficacy of our developed methods on
a range of Bayesian computation problems, including parameter estimation for dynamical
systems, PDE-constrained inverse problems, and Bayesian filtering.  

Details for zoom link below: 

Topic: One School Seminar Time: Aug 22, 2024 01:00 PM Canberra, Melbourne, Sydney 

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