Science Journal Of Mathematics and Statistics, Volume
2013 (2013), July 2013
© Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 License.
Overview of Bayesian Network
Author: Loc Nguyen
University of Technology, Ho Chi Minh city, Vietnam.
Accepted 4 June, 2013; Available Online 15 July 2013
Bayesian network is applied widely in machine learning, data mining,
diagnosis, etc; it has a solid evidence-based inference which is familiar to
human intuition. However Bayesian network causes a little confusion
because there are many complicated concepts, formulas and diagrams
relating to it. Such concepts should be organized and presented in clear
manner so as to be easy to understand it. This is the goal of this report.
This report includes 4 main parts that cover principles of Bayesian
Part 1: Introduction to Bayesian network giving some basic concepts.
Part 2: Bayesian network inference discussing inference mechanism inside Bayesian network.
Part 3: Parameter learning tells us how to update parameters of Bayesian network.
Part 4: Structure learning surveys some main techniques to build up Bayesian network.
Keyword:Bayesian network, parameter learning, structure learning