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:‎‎.‎‎.‎‎.‎