Bayesian Modeling in Bioinformatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Modeling in Bioinformatics write by Dipak K. Dey. This book was released on 2010-09-03. Bayesian Modeling in Bioinformatics available in PDF, EPUB and Kindle. Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c
Bayesian Methods in Structural Bioinformatics
Bayesian Methods in Structural Bioinformatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Methods in Structural Bioinformatics write by Thomas Hamelryck. This book was released on 2012-03-23. Bayesian Methods in Structural Bioinformatics available in PDF, EPUB and Kindle. This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.
Probabilistic Modeling in Bioinformatics and Medical Informatics
Probabilistic Modeling in Bioinformatics and Medical Informatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Modeling in Bioinformatics and Medical Informatics write by Dirk Husmeier. This book was released on 2006-05-06. Probabilistic Modeling in Bioinformatics and Medical Informatics available in PDF, EPUB and Kindle. Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
Bayesian Inference for Gene Expression and Proteomics
Bayesian Inference for Gene Expression and Proteomics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Gene Expression and Proteomics write by Kim-Anh Do. This book was released on 2006-07-24. Bayesian Inference for Gene Expression and Proteomics available in PDF, EPUB and Kindle. Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Bayesian Statistical Methods
Bayesian Statistical Methods - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Statistical Methods write by Brian J. Reich. This book was released on 2019-04-12. Bayesian Statistical Methods available in PDF, EPUB and Kindle. Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
