Statistics and Data Science Seminar
Jie Jian
University of Chicago
Bayesian non-negative tensor factorization for international trading
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.
Wednesday February 25, 2026 at 4:15 PM in 636 SEO