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Nonparametric Bayesian Inference in Biostatistics

Nonparametric Bayesian Inference in Biostatistics

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  • Forlagets pris kr. 1.019,95
  • Leveringstid 2-4 uger
  • Forventet levering 25-05-2018
Bog, paperback (kr. 714,95)
  1. Beskrivelse

    As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. 1 Paperback 22 Table... Læs mere

    Udgivelsesdato:
    15-10-2016
    Leveringstid:
    2-4 uger
    Rating:
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  2. Yderligere info
    Udgivelsesdato:
    15-10-2016
    Sprog:
    Engelsk
    ISBN13:
    9783319368177
    Sidetal:
    465
    Vægt:
    826
    Bredde:
    155
    Længde:
    235
    Mærkat:
    Bog, paperback
    Format:
    Paperback
    Udgave:
    Softcover reprint of the original 1st ed. 2015
    • Bibliotekernes beskrivelse

      As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.

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