@techreport{heckerman1995learning,
author = {Heckerman, David and Geiger, Dan},
title = {Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains},
year = {1995},
month = {August},
abstract = {We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. Inparticular, we unify the approaches we presented at last years conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric appropriate for both domains. We then use this metric in combination with wellknown statistical facts about the Dirichlet and normal Wishart distributions to derive our metrics for discrete and Gaussian domains.},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-bayesian-networks-unification-discrete-gaussian-domains/},
edition = {Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)},
number = {UAI-P-1995-PG-274-284},
note = {Robotics Science and System Workshop on Task and Motion Planning},
}