@inproceedings{d49bc1ad1ab344119adbdf9072edd4b9,
title = "Upper bounds for variational stochastic complexities of Bayesian networks",
abstract = "In recent years, variational Bayesian learning has been used as an approximation of Bayesian learning. In spite of the computational tractability and good generalization performance in many applications, its statistical properties have yet to be clarified. In this paper, we analyze the statistical property in variational Bayesian learning of Bayesian networks which are widely used in information processing and uncertain artificial intelligence. We derive upper bounds for asymptotic variational stochastic complexities of Bayesian networks. Our result theoretically supports the effectiveness of variational Bayesian learning as an approximation of Bayesian learning.",
author = "Kazuho Watanabe and Motoki Shiga and Sumio Watanabe",
year = "2006",
doi = "10.1007/11875581_17",
language = "English",
isbn = "3540454853",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "139--146",
booktitle = "Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings",
note = "7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 ; Conference date: 20-09-2006 Through 23-09-2006",
}