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.
“A graceful ethnographic account that speaks to broad concerns within medical anthropology . . . a remarkable contribution to Tibetan Studies.” —Sienna R. Craig, author of Healing Elements Traditional medicine enjoys widespread appeal in today’s Russia, an appeal that has often been framed either as a holdover from pre-Soviet times or as the symptom of capitalist growing pains and vanishing Soviet modes of life. Mixing Medicines seeks to reconsider these logics of emptiness and replenishment. Set in Buryatia, a semi-autonomous indigenous republic in Southeastern Siberia, the book offers an ethnography of the institutionalization of Tibetan medicine, a botanically-based therapeutic practice framed as at once foreign, international, and local to Russia’s Buddhist regions. By highlighting the cosmopolitan nature of Tibetan medicine and the culturally specific origins of biomedicine, the book shows how people in Buryatia trouble entrenched center-periphery models, complicating narratives about isolation and political marginality. Chudakova argues that a therapeutic life mediated through the practices of traditional medicines is not a last-resort response to sociopolitical abandonment but depends on a densely collective mingling of human and non-human worlds that produces new senses of rootedness, while reshaping regional and national conversations about care, history, and belonging. “In this insightful and well-written ethnography, Tatiana Chudakova shows the elusiveness of Tibetan medicine as Siberia’s Buryat minority seeks to maintain the practice’s integrity and their status as a unique group while also striving to be a part of the Russian nation. Carefully researched and meticulously argued, Mixing Medicines offers a nuanced case for the intimate ties between today’s Russia and Inner Asia.” —Manduhai Buyandelger, author of Tragic Spirit
DNA methylation is a cryptic phenomenon that invokes the methylation of the cytosines in nuclear DNA and is responsible for a wide variety of essential processes, starting from cellular differentiation (embryogenesis), transposon silencing, miRNA dependent methylation and gene regulation. This book presents an overview of different aspects of DNA methylation with a focus on its basic principles and mechanisms and gene silencing. Also discussed, is the role of DNA methylation in plants; epigenetic control of circadian clock operation; photoperiodic flowering; and DNA methylation in cancer and its role in multiple sclerosis.
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.
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