Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

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(as of Dec 27,2024 01:27:39 UTC – Details)

Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)

Probabilistic graphical models (PGMs) have gained significant attention in the field of artificial intelligence and machine learning due to their ability to model complex relationships between variables and make probabilistic inferences. In the book “Probabilistic Graphical Models: Principles and Applications,” authors explore the fundamental principles behind PGMs and their applications in computer vision and pattern recognition.

The book covers topics such as Bayesian networks, Markov networks, and inference algorithms, providing a comprehensive understanding of the theoretical foundations of PGMs. Readers will learn how to use PGMs to model real-world problems, such as image recognition, object tracking, and natural language processing.

With contributions from leading experts in the field, this book is a valuable resource for researchers, practitioners, and students interested in understanding the principles and applications of probabilistic graphical models in computer vision and pattern recognition. Whether you are new to the field or looking to deepen your knowledge, this book offers a comprehensive overview of PGMs and their practical applications.
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