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About
President
Governance
Partner Institutions
Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
Research Groups
Courses
Seminars
Join Us
Faculty
Postdocs
Students
Events
Conferences
Workshops
Forum
Life @ BIMSA
Accommodation
Transportation
Facilities
Tour
News
News
Announcement
Downloads
Qiuzhen College, Tsinghua University
Yau Mathematical Sciences Center, Tsinghua University (YMSC)
Tsinghua Sanya International  Mathematics Forum (TSIMF)
Shanghai Institute for Mathematics and  Interdisciplinary Sciences (SIMIS)
BIMSA > Seminar on Control Theory and Nonlinear Filtering A Convergent Scheme for the Bayesian Filtering Problem Based on the Fokker-Planck Equation and Deep Splitting
A Convergent Scheme for the Bayesian Filtering Problem Based on the Fokker-Planck Equation and Deep Splitting
Organizer
Shing Toung Yau
Speaker
Zeju Sun
Time
Thursday, January 30, 2025 9:00 PM - 9:30 PM
Venue
Online
Abstract
A numerical scheme for approximating the nonlinear filtering density is introduced and its convergence rate is established, theoretically under a parabolic Hormander condition, and empirically in two numerical examples. For the prediction step, between the noisy and partial measurements at discrete times, the scheme approximates the Fokker–Planck equation with a deep splitting scheme, combined with an exact update through Bayes’ formula. This results in a classical prediction-update filtering algorithm that operates online for new observation sequences post-training. The algorithm employs a sampling-based Feynman–Kac approach, designed to mitigate the curse of dimensionality. The convergence proof relies on stochastic integration by parts from the Malliavin calculus. As a corollary we obtain the convergence rate for the approximation of the Fokker–Planck equation alone, disconnected from the filtering problem.
Beijing Institute of Mathematical Sciences and Applications
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