This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature.In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field.
Many novel application scenarios and architectures in business process management or service composition are characterized by a distribution of activities and resources, and by complex interaction and coordination dynamics. In this book, Montali answers fundamental questions on open and declarative modeling abstractions via the integration and extension of quite diverse approaches into a computational logic-based comprehensive framework. This framework allows non IT experts to graphically specify interaction models that are then automatically transformed into a corresponding formal representation and a set of fully automated sound and complete verification facilities. The book constitutes a revised and extended version of the author's PhD thesis, which was honored with the 2009 "Marco Cadoli" prize, awarded by the Italian Association for Logic Programming for the most outstanding thesis focusing on computational logic, discussed between the years 2007 and 2009.
Through years of consulting, operating their own companies, and working in a number of corporate environments, the authors have developed insights into which methods work and which do not in the "real world." The result of this experience is the development of these fundamental rules for building analysis models to solve complex corporate problems. This book describes some common-sense principles that can be used to ensure that the models and solutions that are developed meet the decision maker's needs. These principles will help you avoid mistakes and help you develop better usable solutions.
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