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About
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Governance
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Visit
People
Management
Faculty
Postdocs
Visiting Scholars
Staff
Research
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Courses
Seminars
Join Us
Faculty
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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 Brownian and Poisson Bridges: Application to Nonlinear Filtering Problems and Error Estimates
Brownian and Poisson Bridges: Application to Nonlinear Filtering Problems and Error Estimates
Organizer
Shing Toung Yau
Speaker
Wenhui Dong
Time
Saturday, December 28, 2024 9:00 PM - 9:30 PM
Venue
Online
Abstract
The nonlinear filtering (NLF) problems described by the stochastic systems with jump diffusive state/observation processes have been attracted more and more attentions. In this paper, we consider the NLF problem modelled by a diffusive state process with the mixed observations and the correlated noises. One of the observation processes is driven by the Brownian motion correlated with the state process, and the other one is an independent Poisson point process. The state's unnormalized density conditioned on the continuous observation history is described by the Zakai equation. However, in whatever algorithm, the unnormalized density conditioned only on the sub-filtration generated by the discretized observations can be implemented to approximate the solution of the Zakai equation. The main contribution of this paper is that we show under certain conditions the mean square error of this approximation is no more than the order O(√h), where h is the time step, by the technique of Brownian and Poisson bridges. To verify this theoretical convergence rate, we extend the on- and off-line algorithm, originally proposed for the classical NLF problems, to those with the mixed observations and the correlated noises. This algorithm is numerically experimented in the modified cubic sensor problem, which can achieve the error of the order O(√h). Moreover, we compare this algorithm with the particle filter to illustrate the superiority of the on- and off-line algorithm in both accuracy and efficiency.
Beijing Institute of Mathematical Sciences and Applications
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