Yau-YauAL: A computer tool for solving nonlinear filtering problems
Yu Wang, Shuyuan Xu, Xueda Wei, Xinrui Luo, Stephen Shing-Toung Yau, Shing-Tung Yau, Rongling Wu

TL;DR
YauYauAL is an R-based software package that simplifies the implementation of the Yau-Yau nonlinear filter, enabling real-time visualization and application to complex stochastic systems across various scientific fields.
Contribution
It introduces a user-friendly, open-source software tool combining R and C++ for efficient nonlinear filtering, making advanced methods accessible to practitioners without specialized programming skills.
Findings
Provides real-time parameter adjustment and visualization
Employs finite difference methods for stable solutions
Facilitates broader application of nonlinear filtering
Abstract
The Yau-Yau nonlinear filter has increasingly emerged as a powerful tool to study stochastic complex systems. To leverage it to a wider spectrum of application scenarios, we pack the Yau-Yau filtering ALgorithms (YauYauAL) into a package of computer software. Yau-YauAL was written in R, designed to simplify the implementation of the Yau-Yau filter for solving nonlinear filtering problems. Combining R's accessibility with C++ (via Rcpp) for computational efficiency, YauYauAL provides an intuitive Shiny-based interface that enables real-time parameter adjustment and result visualization. At its core, YauYauAL employs finite difference methods to numerically solve the Kolmogorov forward equation, ensuring a stable and accurate solution even for complex systems. YauYauAL's modular design and open-source framework further encourage customization and community-driven development. YauYauAL…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModel Reduction and Neural Networks · Advanced Adaptive Filtering Techniques · Target Tracking and Data Fusion in Sensor Networks
