Probabilistic Graphical Models: Principles and Techniques 1st Edition Book, ISBN 9780262013192

The Probabilistic Graphical Models: Principles and Techniques 1st Edition Book (9780262013192) provides an authoritative and comprehensive guide to the field of probabilistic graphical models. It is designed for students, researchers and professionals who will go deep into the principles and techniques used to model uncertainty and complex systems in a structured and intuitive way. It covers the basic theory, methodology, and applications of probabilistic reasoning, machine learning, and artificial intelligence, and is written by two leading experts in the field, Daphne Koller and Nir Friedman. The Probabilistic Graphical Models: Principles and Techniques 1st Edition Book introduces the readers to some of the key concepts involving Bayesian networks, Markov random fields, and factor graphs; it also covers advanced topics like inference algorithms, parameter estimation, and learning in graphical models. This 1st edition (9780262013192) strikes a balance between theory and practice, with explanation, illustrative examples, and detailed exercises to reinforce learning. Whether one is interested in the use of probabilistic graphical models in healthcare, finance or robotics, this book covers the tools needed to come up with robust solutions for complex real-world problems. The book Probabilistic Graphical Models: Principles and Techniques, 1st Edition, ISBN 9780262013192, has its reputation as probably the one indispensable reference book now because of its formal explanation in a nicely accessible manner. It covers about 1,200 pages presenting well-organized material at basically all levels on the subject, from non-expert/novice readers, advanced practitioners up to experienced users. Be it a data scientist, academic, or industry professional, this is the most worthy addition to your library. Get your copy of Probabilistic Graphical Models: Principles and Techniques 1st Edition Book (9780262013192) with us now and take the first step toward mastering probabilistic graphical models!