A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces

We introduce an innovative method for incremen- tal nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages slices from high- dimensional surfaces to efficiently approximate posterior distri- butions of any shape. Unlike many existing graph-based meth- ods, our slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate rep- resentation of posterior distributions. Additionally, we propose a novel heuristic to balance between accuracy and efficiency, enabling real-time operation in nonparametric scenarios. In empirical evaluations on synthetic and real-world datasets, our slices approach consistently outperforms other state-of-the-art methods. It demonstrates superior accuracy and achieves a significant reduction in computational complexity, often by an order of magnitude.