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

TL;DR

This paper presents a novel slices-based method for incremental nonparametric inference in high-dimensional spaces, offering improved accuracy and efficiency without intermediate reconstructions, suitable for real-time applications.

Contribution

It introduces a slices perspective that simplifies high-dimensional inference and a heuristic for balancing accuracy and efficiency, outperforming existing methods.

Findings

Outperforms state-of-the-art methods in accuracy

Reduces computational complexity by an order of magnitude

Enables real-time nonparametric inference

Abstract

We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages \slices from high-dimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our \slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate representation 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.