NeurIPS 1997
Estimating Dependency Structure as a Hidden Variable
Abstract
This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors.
Authors
Keywords
No keywords are indexed for this paper.
Context
- Venue
- Annual Conference on Neural Information Processing Systems
- Archive span
- 1987-2025
- Indexed papers
- 30776
- Paper id
- 289521450814148892