Laplace Method for Optimal Bayesian Experimental Design with Applications in Impedance Tomography and Seismic Source Inversion

02:00 PM - 03:00 PM
Quan Long, Joint Postdoctoral Researcher, University of Texas, Austin, and King Abdullah University of Science and Technology, KSA
Computer Science and Mathematics Division Seminar
Joint Institute for Computational Sciences (Building 5100), Auditorium (Room 128)
Email: Clayton Webster
Phone: 865.574.3649

Laplace method is a widely used method to approximate an integration in statistics. We analyze this method in the context of optimal Bayesian experimental design and extend this method from the classical scenario, where parameters can be completely determined by the experiment, to the scenarios where an unidentifiable parametric manifold exists. We show that by carrying out this approximation the estimation of the expected Kullback-Leibler divergence can be significantly accelerated. The developed methodology has been applied to the optimal experimental design of impedance tomography and seismic source inversion.

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