Probabilistic Graphical Models: Principles And Techniques Hardcover
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size
23.42 x 20.78 x 5.21 cm
Product Information
Ideal For
General Readers, Fiction Lovers, Mythology & Historical Fiction Readers, Indian Mythology Enthusiasts, Young Adults (YA) & Adults, College Students, Competitive Exam Aspirants (General Reading / Culture), Gift Purpose (Books Gift Set), Spiritual & Philosophy Interest Readers, Library / Book Collection Buyers
Country Of Origin
India
About Author
Daphne Koller Is Professor In The Department Of Computer Science At Stanford University.
Author Name
Daphne Koller
Genre
Science & Mathematics
No Of Pages
1270
Publisher
Cambridge University Press
Product Description
- Premium Quality: Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data.
- Product Design: For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques.
- User Experience: The book considers the use of the proposed framework for causal reasoning and decision making under uncertainty.
- Versatile Occasion: The main text in each chapter provides the detailed technical development of the key ideas, and most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text.