Work

Fitting mixtures of von Mises distributions via noise contrastive estimation

Year: 2025

Type: article

Abstract: Directional distributions requires the evaluation of complicated normalizing constants, even for the univariate von Mises. For this reason, maximum likelihood estimation methods are often difficult to apply in practice. To address this issue, we present an approach based on Noise Contrastive Estimation (NCE), a statistical learning technique used for parameter estimation in non-normalized statistical models. In NCE, the estimation problem is reformulated as a binary classification task. In this paper, we focus on fitting mixtures of von Mises distributions, with particular emphasis on toroidal data. Our application to real data, in which we compare several estimation methods, suggests that NCE is a promising alternative for parameter inference in finite mixtures of directional distributions.

Source: Statistics & Probability Letters

DOI: 10.1016/j.spl.2025.110608

Authors: Cinzia Di Nuzzo, Salvatore Ingrassia, Luca Scaffidi Domianello

Institution: University of Catania

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,180

Funders: European Commission, Ministero dell'Istruzione e del Merito