Nonparametric Bayesian Inference in Biostatistics (2015)

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    Nonparametric Bayesian (BNP) approaches are becoming increasingly more common in biostatistical inference. Many problems involve an abundance of data that allows the use of more flexible and complex probability models beyond traditional parametric families. One of the most traditional application areas for BNP is in survival analysis, including in particular survival regression. The nature of the recorded outcomes makes it natural to target inference on an entire unknown distribution, rather than focus on just a mean function. Many more recent applications of BNP in biostatistics and bioinformatics involve inference on unknown partitions. For example, this could be an arrangement of patients into clinically meaningful subpopulations. This volume covers these and some more applications of BNP in biomedical inference problems. The intention of this book is to provide a good review of and introduction to related application areas.


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