scholarly journals B-COURSE: A WEB-BASED TOOL FOR BAYESIAN AND CAUSAL DATA ANALYSIS

2002 ◽  
Vol 11 (03) ◽  
pp. 369-387 ◽  
Author(s):  
PETRI MYLLYMÄKI ◽  
TOMI SILANDER ◽  
HENRY TIRRI ◽  
PEKKA URONEN
Keyword(s):  
Data Analysis ◽  
Bayesian Network ◽  
Network Models ◽  
Causal Modeling ◽  
Analysis Tool ◽  
Support Material ◽  
Online Data ◽  
Web Based ◽  
User Friendly ◽  
Bayesian Network Models

B-Course is a free web-based online data analysis tool, which allows the users to analyze their data for multivariate probabilistic dependencies. These dependencies are represented as Bayesian network models. In addition to this, B-Course also offers facilities for inferring certain type of causal dependencies from the data. The software uses a novel "tutorial stylerdquo; user-friendly interface which intertwines the steps in the data analysis with support material that gives an informal introduction to the Bayesian approach adopted. Although the analysis methods, modeling assumptions and restrictions are totally transparent to the user, this transparency is not achieved at the expense of analysis power: with the restrictions stated in the support material, B-Course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of Bayesian and causal modeling. B-Course can be used with most web-browsers (even Lynx), and the facilities include features such as automatic missing data handling and discretization, a flexible graphical interface for probabilistic inference on the constructed Bayesian network models (for Java enabled browsers), automatic prettyHyphen;printed layout for the networks, exportation of the models, and analysis of the importance of the derived dependencies. In this paper we discuss both the theoretical design principles underlying the B-Course tool, and the pragmatic methods adopted in the implementation of the software.

scholarly journals AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

2000 ◽  
Vol 13 ◽  
pp. 155-188 ◽  
Author(s):  
J. Cheng ◽  
M. J. Druzdzel
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Importance Sampling ◽  
Network Models ◽  
Evidential Reasoning ◽  
The Other ◽  
Sampling Algorithm ◽  
Importance Function ◽  
Sampling Algorithms ◽  
Bayesian Network Models

Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

scholarly journals Classifying antimicrobial and multifunctional peptides with Bayesian network models

2018 ◽  
Vol 110 (4) ◽  
pp. e24079 ◽  
Author(s):  
Rainier Barrett ◽  
Shaoyi Jiang ◽  
Andrew D. White
Keyword(s):  
Bayesian Network ◽  
Network Models ◽  
Multifunctional Peptides ◽  
Bayesian Network Models

Bayesian Network Modeling of Transcription Factor Binding Sites

2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung
Keyword(s):  
Transcription Factor ◽  
Parameter Estimation ◽  
Bayesian Network ◽  
Network Modeling ◽  
Network Models ◽  
Transcription Factor Binding Sites ◽  
Physical Problem ◽  
Network Applications ◽  
Bayesian Network Modeling ◽  
Bayesian Network Models

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.

scholarly journals Bayesian Network Models in Cyber Security: A Systematic Review

2017 ◽  
pp. 105-122 ◽  
Author(s):  
Sabarathinam Chockalingam ◽  
Wolter Pieters ◽  
André Teixeira ◽  
Pieter van Gelder
Keyword(s):  
Systematic Review ◽  
Bayesian Network ◽  
Cyber Security ◽  
Network Models ◽  
Bayesian Network Models
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