Bayesian Networks are a local factorization of the joint probability distribution, combined with a directed acyclic graph that represents conditional independence statements. Bayesian networks have become a pervasive methodology in the modeling of uncertain domains because Bayesian networks systematically describe and simplify complex dependencies between random variables in an efficient and principled manner. Founded on the centuries-old Bayesian probability theory, advances in statistics and computer science have allowed Bayesian networks to be built and applied over a wide range of applications in an efficient and intuitive manner. Many examples are presented to demonstrate the capabilities of the networks, including applications to handwriting analysis, medical diagnosis, recommender systems, ecommerce, and credit risk.
|Commitee:||Safer, Alan, Suaray, Kagba|
|School:||California State University, Long Beach|
|Department:||Natural Sciences and Mathematics|
|School Location:||United States -- California|
|Source:||MAI 53/06M(E), Masters Abstracts International|
|Subjects:||Statistics, Artificial intelligence|
|Keywords:||Bayesian Networks, Credit risk, Handwriting analysis, Probablistic graphical models|
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