Data On Stage.com
This site is dedicated to providing
for Bayesian Networks and related techniques.
What are Bayesian Networks?
Bayesian Networks (BNs) are a tool for modeling systems containing
uncertainty that has gained much popularity in recent years. BNs
tools from probability theory (primarily Bayes' theorem, which gave
them their name) to solve various tasks in the areas of data mining and
For more information, see
Soft Discretization for Bayesian Networks
The most recent contribution of this site is a research report on how to implement soft
discretization for Bayesian Networks and the implementation of
the corresponding algorithms as the BNT
A) Research Report Title: A Probability-Based Approach to Soft
Description: This report discusses
The method consists of a soft discretization step that converts the
continuous variables of the training cases into soft evidence, followed
by a suitable parameter learning algorithm for the Bayesian
- How to implement soft discretization to train a discrete Bayesian
Network directly from continuous data.
- How to use soft discretization also for inference and how to
convert the inference results from the discrete network to meaningful
continuous output values.
Most existing soft discretization approaches for Bayesian Networks use
fuzzy set theory which is based on membership functions. In
contrast out method starts out with a probability density function that
spreads the influence of a continuous variable to its neighbors,
followed by a discretization step. Thus our approach to soft
discretization is based on probability theory, rather than fuzzy set
theory. It turns out that a membership function can be
generated from the probability density function through convolution,
yielding a set of probability-based membership functions.
Last updated: Sept 22,
This file can also be downloaded from the Georgia Tech smartech
B) Implementation available:
BNT Soft Discretization Package - NEW!
Last updated: Nov 10, 2009
Donwload: Documentation and source code
/ LinkConnectionStrength Package
Another contribution is a visualization
for link strength and
connection strength in discrete Bayesian Networks.
The LinkStrength /
LinkConnectionStrength package calculates and visualizes link
strengths and connection strengths in discrete Bayesian Networks.
Measures include Entropy, Mutual Information, True Average Link
Blind Average Link Strength.
Implementations of this package are available for
- Matlab's Bayes Net Toolbox
- PNL -
Open-Source Probabilistic Networks
Research Reports available:
Last updated: Sept 22,
The MarkovEquivalent package contains a
few functions to generate
- all DAGs represented by a pattern (partially oriented DAG) or
- all DAGs that are Markov equivalent to a DAG.
Implementation of this package is available only for BNT
(Matlab's Bayes Net Toolbox) at MarkovEquivalent
Last updated: Feb 3, 2007
This site and its content are
maintained by Imme
e-mail address: iebert at engr . colostate . edu!
last updated: November 2011