Bayesian Networks - Theory and Applications: Investigating Bayesian networks theory and its diverse applications in various domains of artificial intelligence
Keywords:
Bayesian networks, probabilistic graphical models, artificial intelligenceAbstract
Bayesian networks are probabilistic graphical models that have gained significant attention in artificial intelligence due to their ability to model complex relationships and uncertainty. This paper provides a comprehensive overview of Bayesian networks, starting with their theoretical foundations and extending to various applications across different domains. We discuss the principles behind Bayesian networks, including the representation of conditional dependencies using directed acyclic graphs and the use of Bayes' theorem for probabilistic inference.
The paper then explores the wide range of applications of Bayesian networks in artificial intelligence, including but not limited to medical diagnosis, natural language processing, computer vision, and anomaly detection. We examine how Bayesian networks are utilized in these domains to model complex systems, make predictions, and perform reasoning under uncertainty. Additionally, we discuss the challenges and future directions in the field of Bayesian networks, such as scalability, learning from data, and integrating with other AI techniques.
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