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  • ISBN: 9780262600323
  • ISBN10: 0262600323

Learning in Graphical Models

by Michael Irwin Jordan

  • List Price: $80.00
  • Binding: Paperback
  • Publisher: Mit Pr
  • Publish date: 01/01/1999
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Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering -- uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.
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Missing back cover and part of index. Reading copy with considerable wear.
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8vo-over 7¾"-9¾" tall Paperback, 634 pp. Solid fine, clean.
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