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Probabilistic Graphical Models: Principles and Techniques

Probabilistic Graphical Models: Principles and Techniques

Probabilistic Graphical Models: Principles and Techniques
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Probabilistic Graphical Models: Principles and Techniques Hardback - 2025

by Ignacio Zurrian

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Hardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; Foundations of Probabilistic Graphical Models: Provide a comprehensive introduction to probabilistic graphical models (PGMs), including their purpose, fundamental concepts, and types (e.g., Bayesian networks, Markov networks). Discuss t
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Details

  • Title Probabilistic Graphical Models: Principles and Techniques
  • Author Ignacio Zurrian
  • Binding Hardback
  • Condition New
  • Pages 278
  • Volumes 1
  • Language ENG
  • Publisher Kruger Brentt Publisher Uk. Ltd.
  • Publication date 2025-01-01
  • Bookseller's Inventory # ria9781787153783_inp
  • ISBN 9781787153783 / 1787153789
  • Weight 1.54 lbs (0.70 kg)
  • Dimensions 10 x 7 x 0.69 in (25.40 x 17.78 x 1.75 cm)
  • Category Computers - General Information
  • Quantity available 842

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From the publisher

Foundations of Probabilistic Graphical Models: Provide a comprehensive introduction to probabilistic graphical models (PGMs), including their purpose, fundamental concepts, and types (e.g., Bayesian networks, Markov networks). Discuss the representation of complex probability distributions and dependencies using graphical structures.

Graph Theory and Probability Foundations: Explore the underlying graph theory and probability principles essential for understanding PGMs. Cover topics such as nodes, edges, directed and undirected graphs, conditional independence, and joint probability distributions. Provide insights into how these principles are used to model real-world problems.

Applications and Case Studies: Examine the applications of PGMs in various domains such as machine learning, computer vision, natural language processing, and bioinformatics. Provide case studies and examples to illustrate how PGMs are used to solve practical problems and make predictions based on complex data.

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