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Nonparametric Bayesian Inference in Biostatistics : Mathematics and Statistics (R0) - Peter Müller

Nonparametric Bayesian Inference in Biostatistics

By: Peter Müller (Editor), Riten Mitra (Editor)

eText | 25 July 2015

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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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Non-FictionMedicineMedicine in GeneralPublic Health & Preventive MedicineEpidemiology & Medical StatisticsMathematicsProbability & StatisticsScienceBiology, Life SciencesLife Sciences in General

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