This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book's structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.

This report is designed as a practical guide to help you and your firm get to grips with process improvement techniques, and to understand their core benefits and practical applications in a legal environment. With contributions from leading law firms, consultants, and internationally renowned experts on legal process improvement and project management, this report: Provides in-depth, strategic, and tactical guidance on the application of process improvement in law firms; Outlines the different approaches firms are taking, and includes case studies highlighting what the results have been for those who have already adopted process improvement techniques; Includes practical guidance on implementing process improvement - from gaining buy-in through to process mapping and devising different strategies; and Explains the relationship between legal process improvement and related disciplines and key methodologies such as Lean and Six Sigma, project management, and KM.

This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.