Parameter Learning for Bayesian Networks with Strict Qualitative Influences
author: Ad Feelders,
Utrecht University
published: Oct. 8, 2007, recorded: September 2007, views: 3402
published: Oct. 8, 2007, recorded: September 2007, views: 3402
Slides
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
We propose a new method for learning the parameters of a Bayesian network with qualitative influences. The proposed method aims to remove unwanted (context-specific) independencies that are created by the order-constrained maximum likelihood (OCML) estimator. This is achieved by averaging the OCML estimator with the fitted probabilities of a first-order logistic regression model. We show experimentally that the new learning algorithm does not perform worse than OCML, and resolves a large part of the independencies.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: