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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2373

Title: Bayesian Networks
Authors: Nashad Abdul Rahiman
Keywords: Bayesian Networks
directed acyclic graph (DAG)
Bayesian Network and Artificial Intelligence
Issue Date: 21-Mar-2011
Abstract: Bayesian networks provide a means of parsimoniously expressing joint probability distributions over many interrelated hypotheses. A Bayesian network consists of a directed acyclic graph (DAG) and a set of local distributions. Bayesian Networks are becoming an increasingly important area for research and application in the entire field of Artificial Intelligence. This paper explores the nature and implications for Bayesian Networks beginning with an overview and comparison of inferential statistics and Bayes' Theorem. The nature, relevance and applicability of Bayesian Network theory for issues of advanced computability forms the core of the current discussion. A number of current applications using Bayesian networks is examined. The paper concludes with a brief discussion of the appropriateness and limitations of Bayesian Networks for human-computer interaction and automated learning.
Description: Seminar report submitted in 2010 in partial fulfillment of the requirements for the Degree of Bachelor of Technology (B.Tech ) in Computer Science and Engineering under the Guideship of Sudheep Elayidom.
URI: http://hdl.handle.net/123456789/2373
Appears in Collections:Seminar Reports

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