Convergence of the EM algorithm via proximal techniques
Abstract
We investigate convergence of the expectation maximization algorithm by representing it as a generalized proximal method. Convergence of iterates and not just in value is investigated under natural hypotheses such as definability of the incomplete data log-likelihood in the sense of o-minimal structure theory.
Fields of Study & Subjects
Machine Learning and Algorithms
Neural Networks and Applications
Markov Chains and Monte Carlo Methods
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 5 January 2026
- Document type
- Preprint