The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Bayesian Networks
|
|
Author(s): Ahmad Bashir (University of Texas at Dallas, USA), Latifur Khan (University of Texas at Dallas, USA)and Mamoun Awad (University of Texas at Dallas, USA)
Copyright: 2005
Pages: 5
Source title:
Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch018
PurchaseView Bayesian Networks on the publisher's website for pricing and purchasing information. |
Abstract
A Bayesian network is a graphical model that finds probabilistic relationships among variables of a system. The basic components of a Bayesian network include a set of nodes, each representing a unique variable in the system, their inter-relations, as indicated graphically by edges, and associated probability values. By using these probabilities, termed conditional probabilities, and their interrelations, we can reason and calculate unknown probabilities. Furthermore, Bayesian networks have distinct advantages compared to other methods, such as neural networks, decision trees, and rule bases, which we shall discuss in this paper.
Related Content
|
Md Sakir Ahmed, Abhijit Bora.
© 2024.
15 pages.
|
|
Lakshmi Haritha Medida, Kumar.
© 2024.
18 pages.
|
|
Gypsy Nandi, Yadika Prasad.
© 2024.
16 pages.
|
|
Saurav Bhattacharjee, Sabiha Raiyesha.
© 2024.
14 pages.
|
|
Naren Kathirvel, Kathirvel Ayyaswamy, B. Santhoshi.
© 2024.
26 pages.
|
|
K. Sudha, C. Balakrishnan, T. P. Anish, T. Nithya, B. Yamini, R. Siva Subramanian, M. Nalini.
© 2024.
25 pages.
|
|
Sabiha Raiyesha, Papul Changmai.
© 2024.
28 pages.
|
|
|