arXiv · 2601.06830
Constrained Density Estimation via Optimal Transport
Abstract
A novel framework for density estimation under expectation constraints is proposed. The framework minimizes the Wasserstein distance between the estimated density and a prior, subject to the constraints that the expected value of a set of functions adopts or exceeds given values. The framework is generalized to include regularization inequalities to mitigate the artifacts in the target measure. An annealing-like algorithm is developed to address non-smooth constraints, with its effectiveness demonstrated through both synthetic and proof-of-concept real world examples in finance.
Explore related subjects
Keep this discovery
Yinan Hu, Esteban G. Tabak. 2026-01-11. Constrained Density Estimation via Optimal Transport. https://arxiv.org/abs/2601.06830
Cite the original work for its findings. Save a collection to share your selection of sources.