ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1210.0198
62
45
v1v2 (latest)

Maximum Likelihood for Matrices with Rank Constraints

30 September 2012
Bernd Sturmfels
Jose Rodriguez
Bernd Sturmfels
ArXiv (abs)PDFHTML
Abstract

Maximum likelihood estimation is a fundamental optimization problem in statistics. We study this problem on manifolds of matrices with bounded rank. These represent mixtures of distributions of two independent discrete random variables. We determine the maximum likelihood degree for a range of determinantal varieties, and we apply numerical algebraic geometry (Bertini) to compute all critical points of their likelihood functions. We present an intriguing duality conjecture that seems topological in nature.

View on arXiv
Comments on this paper