Nonparametric Bayesian Inference in Biostatistics (Frontiers in Probability and the Statistical Sciences)

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.

 

正如本書的章節所展示的，非參數貝葉斯方法（BNP）在臨床科學和基因組學中的未知分割等推論問題中具有重要的應用價值。非參數貝葉斯方法在生物統計推論中的應用範圍不斷擴大，從蛋白質組學到臨床試驗。許多研究問題涉及大量數據，需要超越傳統參數方法的靈活和複雜的概率模型。正如本書的專家貢獻者所展示的，非參數貝葉斯方法可以成為解決這些問題的答案。特別是在生存分析中，傳統上使用非參數貝葉斯方法，但現在非參數貝葉斯方法的潛力非常廣泛。這適用於重要任務，如將患者分為臨床意義明確的亞群體，以及將基因組分割為功能上不同的區域。本書旨在回顧並介紹非參數貝葉斯方法的應用領域。儘管現有的書籍提供了理論基礎，但本書通過引人入勝的例子和研究問題將理論與實踐相結合。各章節涵蓋臨床試驗、空間推論、蛋白質組學、基因組學、聚類、生存分析和ROC曲線等主題。