Parameter estimation of dependence tree models using the EM algorithm
- 1 August 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Signal Processing Letters
- Vol. 2 (8) , 157-159
- https://doi.org/10.1109/97.404132
Abstract
A dependence tree is a model for the joint probability distribution of an n-dimensional random vector, which requires a relatively small number of free parameters by making Markov-like assumptions on the tree. The authors address the problem of maximum likelihood estimation of dependence tree models with missing observations, using the expectation-maximization algorithm. The solution involves computing observation probabilities with an iterative "upward-downward" algorithm, which is similar to an algorithm proposed for belief propagation in causal trees, a special case of Bayesian networks.<>Keywords
This publication has 4 references indexed in Scilit:
- Bayesian Belief Networks as a tool for stochastic parsingSpeech Communication, 1995
- The estimation of stochastic context-free grammars using the Inside-Outside algorithmComputer Speech & Language, 1990
- A tutorial on hidden Markov models and selected applications in speech recognitionProceedings of the IEEE, 1989
- Approximating discrete probability distributions with dependence treesIEEE Transactions on Information Theory, 1968