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  3. Bayesian Nonparametrics
  • Cited by 287
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    • Publisher:
      Cambridge University Press
      Publication date:
      January 2011
      April 2010
      ISBN:
      9780511802478
      9780521513463
      DOI:
      https://doi.org/10.1017/CBO9780511802478
      Dimensions:
      (253 x 177 mm)
      Weight & Pages:
      0.73kg, 308 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Computer Science, Computational Biology and Bioinformatics, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
      Series:
      Cambridge Series in Statistical and Probabilistic Mathematics (28)
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    Subjects:
    Computer Science, Computational Biology and Bioinformatics, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
    Series:
    Cambridge Series in Statistical and Probabilistic Mathematics (28)
    Information

    Book description

    Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.

    Reviews

    "The book looks like it will be useful to a wide range of researchers. I like that there is a lot of discussion of the models themselves as well as the computation. The book, especially in the early chapters, is more theoretical than I would prefer... But, hey, that's just my taste... on the whole I think the book is excellent. If I didn't think the book was important, I wouldn't be spending my time pointing out my disagreements with it!"
    Andrew Gelman, Columbia University

    "The book provides a tour de force presentation of selected topics in an emerging branch of modern statistical science, and not only justfies the reader’s curiosity, but also expands it.... The book brings together a well-structured account of a number of topics on the theory, methodology, applications, and challenges of future developments in the rapidly expanding area of Bayesian nonparametrics. Given the current dearth of books on BNP, this book will be an invaluable source of information and reference for anyone interested in BNP, be it a student, an established statistician, or a researcher in need of flexible statistical analyses."
    Milovan Krnjajic, Journal of the American Statistical Association

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    Contents

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