By Topic

Parameter estimation of dependence tree models using the EM algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ronen, O. ; Coll. of Eng., Boston Univ., MA, USA ; Rohlicek, J.R. ; Ostendorf, M.

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.<>

Published in:

Signal Processing Letters, IEEE  (Volume:2 ,  Issue: 8 )