{"product_id":"bayesian-modeling-using-winbugs-9780470141144","title":"Bayesian Modeling Using WinBUGS","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDetailed examples will be provided ranging from the very basic to the more advanced; they will also reflect realistic data sets (available from the Internet). An underlying emphasis is given to Generalized Linear Models (GLMs) that are familiar to most readers and researchers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003eAcronyms.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction to Bayesian inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction: Bayesian modeling in the 21st century.\u003c\/p\u003e \u003cp\u003e1.2 Definition of statistical models.\u003c\/p\u003e \u003cp\u003e1.3 Bayes theorem.\u003c\/p\u003e \u003cp\u003e1.4 Model-based Bayesian Inference.\u003c\/p\u003e \u003cp\u003e1.5 Inference using conjugate prior distributions.\u003c\/p\u003e \u003cp\u003e1.6 Nonconjugate Analysis.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Markov Chain Monte Carlo Algorithms in Bayesian Inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Simulation, Monte Carlo integration, and their implementation in Bayesian inference.\u003c\/p\u003e \u003cp\u003e2.2 Markov chain Monte Carlo methods.\u003c\/p\u003e \u003cp\u003e2.3 Popular MCMC algorithms.\u003c\/p\u003e \u003cp\u003e2.4 Summary and closing remarks.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. WinBUGS Software: Introduction, Setup and Basic Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction and historical background.\u003c\/p\u003e \u003cp\u003e3.2 The WinBUGS environment.\u003c\/p\u003e \u003cp\u003e3.3 Preliminaries on using WinBUGS.\u003c\/p\u003e \u003cp\u003e3.4 Building Bayesian models in WinBUGS.\u003c\/p\u003e \u003cp\u003e3.5 Compiling the model and simulating values.\u003c\/p\u003e \u003cp\u003e3.6 Basic output analysis using the sample monitor tool.\u003c\/p\u003e \u003cp\u003e3.7 Summarizing the procedure.\u003c\/p\u003e \u003cp\u003e3.8 Chapter summary and concluding comments.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. WinBUGS Software: Illustration, Results, and Further Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 A complete example of running MCMC in WinBUGS for a simple model.\u003c\/p\u003e \u003cp\u003e4.2 Further output analysis using the inference menu.\u003c\/p\u003e \u003cp\u003e4.3 Multiple chains.\u003c\/p\u003e \u003cp\u003e4.4 Changing the properties of a figure.\u003c\/p\u003e \u003cp\u003e4.5 Other tools and menus.\u003c\/p\u003e \u003cp\u003e4.6 Summary and concluding remarks.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Introduction to Bayesian Models: Normal models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 General modeling principles.\u003c\/p\u003e \u003cp\u003e5.2 Model specification in normal regression models.\u003c\/p\u003e \u003cp\u003e5.3 Using vectors and multivariate priors in normal regression models.\u003c\/p\u003e \u003cp\u003e5.4 Analysis of variance models.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Incorporating Categorical Variables in Normal Models and Further Modeling Issues.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Analysis of variance models using dummy variables.\u003c\/p\u003e \u003cp\u003e6.2 Analysis of covariance models.\u003c\/p\u003e \u003cp\u003e6.3 A Bioassay example.\u003c\/p\u003e \u003cp\u003e6.4 Further modeling issues.\u003c\/p\u003e \u003cp\u003e6.5 Closing remarks.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Introduction to Generalized Linear Models: Binomial and Poisson Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Prior distributions.\u003c\/p\u003e \u003cp\u003e7.3 Posterior inference.\u003c\/p\u003e \u003cp\u003e7.4 Poisson regression models.\u003c\/p\u003e \u003cp\u003e7.5 Binomial response models.\u003c\/p\u003e \u003cp\u003e7.6 Models for contingency tables.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Models for Positive Continuous Data, Count Data, and Other GLM-Based Extensions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Models with nonstandard distributions.\u003c\/p\u003e \u003cp\u003e8.2 Models for positive continuous response variables.\u003c\/p\u003e \u003cp\u003e8.3 Additional models for count data.\u003c\/p\u003e \u003cp\u003e8.4 Further GLM-based models and extensions.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Bayesian Hierarchical Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Some simple examples.\u003c\/p\u003e \u003cp\u003e9.3 The generalized linear mixed model formulation.\u003c\/p\u003e \u003cp\u003e9.4 Discussion, closing remarks, and further reading.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. The Predictive Distribution and Model Checking.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Estimating the predictive distribution for future or missing observations using MCMC.\u003c\/p\u003e \u003cp\u003e10.3 Using the predictive distribution for model checking.\u003c\/p\u003e \u003cp\u003e10.4 Using cross-validation predictive densities for model checking, evaluation, and comparison.\u003c\/p\u003e \u003cp\u003e10.5 Illustration of a complete predictive analysis: Normal regression models.\u003c\/p\u003e \u003cp\u003e10.6 Discussion.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Bayesian Model and Variable Evaluation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Prior predictive distributions as measures of model comparison: Posterior model odds and Bayes factors.\u003c\/p\u003e \u003cp\u003e11.2 Sensitivity of the posterior model probabilities: The Lindley-Bartlett paradox.\u003c\/p\u003e \u003cp\u003e11.3 Computation of the marginal likelihood.\u003c\/p\u003e \u003cp\u003e11.4 Computation of the marginal likelihood using WinBUGS.\u003c\/p\u003e \u003cp\u003e11.5 Bayesian variable selection using Gibbs-based methods.\u003c\/p\u003e \u003cp\u003e11.6 Posterior inference using the output of Bayesian variable selection samplers.\u003c\/p\u003e \u003cp\u003e11.7 Implementation of Gibbs variable selection in WinBUGS using an illustrative example.\u003c\/p\u003e \u003cp\u003e11.8 The Carlin Chib’s method.\u003c\/p\u003e \u003cp\u003e11.9 Reversible jump MCMC (RJMCMC).\u003c\/p\u003e \u003cp\u003e11.10 Using posterior predictive densities for model evaluation.\u003c\/p\u003e \u003cp\u003e11.11 Information criteria.\u003c\/p\u003e \u003cp\u003e11.12 Discussion and further reading.\u003c\/p\u003e \u003cp\u003eProblems.\u003c\/p\u003e \u003cp\u003eAppendix A: Model Specification via Directed Acyclic Graphs: The Doodle Menu.\u003c\/p\u003e \u003cp\u003eA.1 Introduction: Starting with DOODLE.\u003c\/p\u003e \u003cp\u003eA.2 Nodes.\u003c\/p\u003e \u003cp\u003eA.3 Edges.\u003c\/p\u003e \u003cp\u003eA.4 Panels.\u003c\/p\u003e \u003cp\u003eA.5 A simple example.\u003c\/p\u003e \u003cp\u003eAppendix B: The Batch Mode: Running a Model in the Background Using Scripts.\u003c\/p\u003e \u003cp\u003eB.1 Introduction.\u003c\/p\u003e \u003cp\u003eB.2 Basic commands: Compiling and running the model.\u003c\/p\u003e \u003cp\u003eAppendix C: Checking Convergence Using CODA\/BOA.\u003c\/p\u003e \u003cp\u003eC.1 Introduction.\u003c\/p\u003e \u003cp\u003eC.2 A short historical review.\u003c\/p\u003e \u003cp\u003eC.3 Diagnostics implemented by CODA\/BOA.\u003c\/p\u003e \u003cp\u003eC.4 A first look of CODA\/BOA.\u003c\/p\u003e \u003cp\u003eC.5 A simple example.\u003c\/p\u003e \u003cp\u003eAppendix D: Notation Summary.\u003c\/p\u003e \u003cp\u003eD.1 MCMC.\u003c\/p\u003e \u003cp\u003eD.2 Subscripts and indices.\u003c\/p\u003e \u003cp\u003eD.3 Parameters.\u003c\/p\u003e \u003cp\u003eD.4 Random variables and data.\u003c\/p\u003e \u003cp\u003eD.5 Sample estimates.\u003c\/p\u003e \u003cp\u003eD.6 Special functions, vectors and matrices.\u003c\/p\u003e \u003cp\u003eD.7 Distributions.\u003c\/p\u003e \u003cp\u003eD.8 Distribution-related notation.\u003c\/p\u003e \u003cp\u003eD.9 Notation used in ANOVA and ANCOVA.\u003c\/p\u003e \u003cp\u003eD.10 Variable and model specification.\u003c\/p\u003e \u003cp\u003eD.11 Deviance information criterion (DIC).\u003c\/p\u003e \u003cp\u003eD.12 Predictive measures.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":53515414864215,"sku":"9780470141144","price":125.96,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/bayesian-modeling-using-winbugs-9780470141144","provider":"Book Curl","version":"1.0","type":"link"}