{"product_id":"bayesian-analysis-of-gene-expression-data-130-statistics-in-practice-9780470517666","title":"Bayesian Analysis of Gene Expression Data 130 Statistics in Practice","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides an introduction to both Bayesian methods and gene expression, accessible to people with backgrounds in either. The text is enhanced by the inclusion of numerous problems and solutions, designed with an emphasis on methodology and application.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“The target audience for this book is clearly statisticians rather than biologists … It does provide a very useful overview of statistical genomics for anyone working in the field.”  (\u003ci\u003eThe Quarterly Review of Biology\u003c\/i\u003e, 1 March 2012)\u003c\/p\u003e \u003cp\u003e\"Bioinformatics researchers from many fields will find much value in this book.\" (Mathematical Reviews, 2011)\u003c\/p\u003e \u003cp\u003e\"Experienced readers will find the review of advanced methods for bioinformatics challenging and attainable. This book will interest graduate students in statistics and bioinformatics researchers from many fields.\" (\u003ci\u003eBook News\u003c\/i\u003e, December 2009)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eTable of Notation.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Bioinformatics and Gene Expression Experiments\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 About This Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Basic Biology\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e2.1 Background.\u003c\/p\u003e \u003cp\u003e2.1.1 DNA Structures and Transcription.\u003c\/p\u003e \u003cp\u003e2.2 Gene Expression Microarray Experiments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Bayesian Linear Models for Gene Expression\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Bayesian Analysis of a Linear Model.\u003c\/p\u003e \u003cp\u003e3.3 Bayesian Linear Models for Differential Expression.\u003c\/p\u003e \u003cp\u003e3.4 Bayesian ANOVA for Gene Selection.\u003c\/p\u003e \u003cp\u003e3.5 Robust ANOVA model with Mixtures of Singular Distributions.\u003c\/p\u003e \u003cp\u003e3.6 Case Study.\u003c\/p\u003e \u003cp\u003e3.7 Accounting for Nuisance Effects.\u003c\/p\u003e \u003cp\u003e3.8 Summary and Further Reading.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Bayesian Multiple Testing and False Discovery Rate Analysis\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e4.1 Introduction to Multiple Testing.\u003c\/p\u003e \u003cp\u003e4.2 False Discovery Rate Analysis.\u003c\/p\u003e \u003cp\u003e4.3 Bayesian False Discovery Rate Analysis.\u003c\/p\u003e \u003cp\u003e4.4 Bayesian Estimation of FDR.\u003c\/p\u003e \u003cp\u003e4.5 FDR and Decision Theory.\u003c\/p\u003e \u003cp\u003e4.6 FDR and bFDR Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Bayesian Classification for Microarray Data\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Classification and Discriminant Rules.\u003c\/p\u003e \u003cp\u003e5.3 Bayesian Discriminant Analysis.\u003c\/p\u003e \u003cp\u003e5.4 Bayesian Regression Based Approaches to Classification.\u003c\/p\u003e \u003cp\u003e5.5 Bayesian Nonlinear Classification.\u003c\/p\u003e \u003cp\u003e5.6 Prediction and Model Choice.\u003c\/p\u003e \u003cp\u003e5.7 Examples.\u003c\/p\u003e \u003cp\u003e5.8 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Bayesian Hypothesis Inference for Gene Classes\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e6.1 Interpreting Microarray Results.\u003c\/p\u003e \u003cp\u003e6.2 Gene Classes.\u003c\/p\u003e \u003cp\u003e6.3 Bayesian Enrichment Analysis.\u003c\/p\u003e \u003cp\u003e6.4 Multivariate Gene Class Detection.\u003c\/p\u003e \u003cp\u003e6.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Unsupervised Classification and Bayesian Clustering\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e7.1 Introduction to Bayesian Clustering for Gene Expression Data.\u003c\/p\u003e \u003cp\u003e7.2 Hierarchical Clustering.\u003c\/p\u003e \u003cp\u003e7.3 \u003ci\u003eK\u003c\/i\u003e-Means Clustering.\u003c\/p\u003e \u003cp\u003e7.4 Model-Based Clustering.\u003c\/p\u003e \u003cp\u003e7.5 Model-Based Agglomerative Hierarchical Clustering.\u003c\/p\u003e \u003cp\u003e7.6 Bayesian Clustering.\u003c\/p\u003e \u003cp\u003e7.7 Principal Components.\u003c\/p\u003e \u003cp\u003e7.8 Mixture Modeling.\u003c\/p\u003e \u003cp\u003e7.8.1 Label Switching.\u003c\/p\u003e \u003cp\u003e7.9 Clustering Using Dirichlet Process Prior.\u003c\/p\u003e \u003cp\u003e7.9.1 Infinite Mixture of Gaussian Distributions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bayesian Graphical Models\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Probabilistic Graphical Models.\u003c\/p\u003e \u003cp\u003e8.3 Bayesian Networks.\u003c\/p\u003e \u003cp\u003e8.4 Inference for Network Models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Advanced Topics\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Analysis of Time Course Gene Expression Data.\u003c\/p\u003e \u003cp\u003e9.3 Survival Prediction Using Gene Expression Data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Basics of Bayesian Modeling\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eA.1 Basics.\u003c\/p\u003e \u003cp\u003eA.1.1 The General Representation Theorem.\u003c\/p\u003e \u003cp\u003eA.1.2 Bayes’ Theorem.\u003c\/p\u003e \u003cp\u003eA.1.3 Models Based on Partial Exchangeability.\u003c\/p\u003e \u003cp\u003eA.1.4 Modeling with Predictors.\u003c\/p\u003e \u003cp\u003eA.1.5 Prior Distributions.\u003c\/p\u003e \u003cp\u003eA.1.6 Decision Theory and Posterior and Predictive Inferences.\u003c\/p\u003e \u003cp\u003eA.1.7 Predictive Distributions.\u003c\/p\u003e \u003cp\u003eA.1.8 Examples.\u003c\/p\u003e \u003cp\u003eA.2 Bayesian Model Choice.\u003c\/p\u003e \u003cp\u003eA.3 Hierarchical Modeling.\u003c\/p\u003e \u003cp\u003eA.4 Bayesian Mixture Modeling.\u003c\/p\u003e \u003cp\u003eA.5 Bayesian Model Averaging.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: Bayesian Computation Tools\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eB.1 Overview.\u003c\/p\u003e \u003cp\u003eB.2 Large-Sample Posterior Approximations.\u003c\/p\u003e \u003cp\u003eB.2.1 The Bayesian Central Limit Theorem.\u003c\/p\u003e \u003cp\u003eB.2.2 Laplace’s Method.\u003c\/p\u003e \u003cp\u003eB.3 Monte Carlo Integration.\u003c\/p\u003e \u003cp\u003eB.4 Importance Sampling.\u003c\/p\u003e \u003cp\u003eB.5 Rejection Sampling.\u003c\/p\u003e \u003cp\u003eB.6 Gibbs Sampling.\u003c\/p\u003e \u003cp\u003eB.7 The Metropolis Algorithm and Metropolis–Hastings.\u003c\/p\u003e \u003cp\u003eB.8 Advanced Computational Methods.\u003c\/p\u003e \u003cp\u003eB.8.1 Block MCMC.\u003c\/p\u003e \u003cp\u003eB.8.2 Truncated Posterior Spaces.\u003c\/p\u003e \u003cp\u003eB.8.3 Latent Variables and the Auto-Probit Model.\u003c\/p\u003e \u003cp\u003eB.8.4 Bayesian Simultaneous Credible Envelopes.\u003c\/p\u003e \u003cp\u003eB.8.5 Proposal Updating.\u003c\/p\u003e \u003cp\u003eB.9 Posterior Convergence Diagnostics.\u003c\/p\u003e \u003cp\u003eB.10 MCMC Convergence and the Proposal.\u003c\/p\u003e \u003cp\u003eB.10.1 Graphical Checks for MCMC Methods.\u003c\/p\u003e \u003cp\u003eB.10.2 Convergence Statistics.\u003c\/p\u003e \u003cp\u003eB.10.3 MCMC in High-Throughput Analysis.\u003c\/p\u003e \u003cp\u003eB.11 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default 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