Stone Soup Goes NUTS: Adding Proposals and the No-U-Turn Sampler to Stone Soup

Particle filters are essential for state estimation in non-linear and non-Gaussian systems, with performance hinging on effective proposal distributions. This paper presents the implementation of Kalman Filter and No-U-Turn Sampler (NUTS) proposals within the Stone Soup Python framework. The Kalman Filter proposal offers computational efficiency for structured systems, while NUTS enables robust exploration of complex, highdimensional distributions. Benchmark evaluations demonstrate the complementary strengths of these methods, enhancing Stone Soup's capabilities for diverse state estimation challenges.