Jie Jian : Bayesian non-negative tensor factorization for international trading

Posted by Kyunghee Han , part of the Statistics and Data Science Seminar.

At

Feb. 25, 2026, 4:15 p.m.

In

636 SEO

Abstract

Detecting dependence structures in international trade—such as persistent exporter–importer affinities, and supply-chain clustering—often relies on latent variable models that summarize high-dimensional trading flows. We propose a novel Bayesian non-negative tensor factorization for large, sparse, nonnegative trading tensors with excess zeros and continuous positive measurements. We target settings with millions of entries and extreme sparsity. Each entry follows a spike-and-slab model: a point mass at zero coupled with a gamma–Poisson construction that yields a low-rank nonnegative decomposition via gamma latent factors. The framework provides interpretable mode-specific components and principled uncertainty quantification.