Nonparametric Automatic Differentiation Variational Inference with Spline Approximation

arXiv:2403.06302v1 Announce Type: cross
Abstract: Automatic Differentiation Variational Inference (ADVI) is efficient in learning probabilistic models. Classic ADVI relies on the parametric approach to approximate the posterior. In this paper, we develop a spline-based nonparametric approximation approach that enables flexible posterior approximation for distributions with complicated structures, such as skewness, multimodality, and bounded support. Compared with widely-used nonparametric variational inference methods, the proposed method is easy to implement and adaptive to various data structures. By adopting the spline approximation, we …

abstract approximation arxiv cs.lg differentiation inference multimodality paper parametric posterior skewness spline stat.ml type