Bayesian Networks
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Aragorn: Publications
Proc. of International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems, in conjunction with MSWiM 2009, Tenerife, Canary Islands, Spain, 2009 2nd IEEE International Workshop on Management of Emerging Networks and Services (IEEE MENS 2010) in conjunction with IEEE Globecom, Miami, Florida, USA December 2010. T.
By Kevin Murphy, 1998. "Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering -- uncertainty and complexity -- and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms.
Graphical Models
Bayes nets
Links in this web page: Conferences | Bayes Nets Construction | Bayes Nets Structure Learning | Database | Groups | Introduction | Journals | References | Researchers | Resources | Software Bayes Nets or Bayesian networks [79] are graphical representation for probabilistic relationships among a set of random variables. Given a finite set of discrete random variables where each variable may take values from a finite set, denoted by . A Bayesian network is an annotated directed acyclic graph (DAG) G that encodes a joint probability distribution over . The nodes of the graph correspond to the random variables .
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Statistical Data Mining Tutorials
A much more detailed comparison of some of these software packages is available from Appendix B of Bayesian AI , by Ann Nicholson and Kevin Korb. This appendix is available here , and is based on the online comparison below. API = application program interface included? (N means the program cannot be integrated into your code, i.e., it must be run as a standalone executable.) Exec = Executable runs on W = Windows (95/98/NT), U = Unix, M = Mac, or - = any machine with a compiler.
Software Packages for Graphical Models / Bayesian Networks
Banjo: Bayesian Network Inference with Java Objects
Banjo is a software application and framework for structure learning of static and dynamic Bayesian networks, developed under the direction of Alexander J. Hartemink in the Department of Computer Science at Duke University . Banjo was designed from the ground up to provide efficient structure inference when analyzing large, research-oriented data sets, while at the same time being accessible enough for students and researchers to explore and experiment with the algorithms.
This documentation was last updated on 29 October 2007. Click here for a French version of this documentation (last updated in 2005). To define a Bayes net, you must specify the graph structure and then the parameters. We look at each in turn, using a simple example (adapted from Russell and Norvig, "Artificial Intelligence: a Modern Approach", Prentice Hall, 1995, p454).