"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" - Information and Links:

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling - Info and Reading Options

"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" was published by Taylor & Francis Group in 2021 - Milton, it has 284 pages and the language of the book is English.


“Using R for Bayesian Spatial and Spatio-Temporal Health Modeling” Metadata:

  • Title: ➤  Using R for Bayesian Spatial and Spatio-Temporal Health Modeling
  • Author:
  • Language: English
  • Number of Pages: 284
  • Publisher: Taylor & Francis Group
  • Publish Date:
  • Publish Location: Milton

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"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" Description:

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Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Biography -- List of Tables -- 1. Introduction and Datasets -- 1.1. Datasets -- 2. R Graphics and Spatial Health Data -- 2.1. Three-dimensional Surface Visualization -- 2.2. Universal Kriging -- 2.3. Thematic Mapping -- 2.3.1. Getting map information into R -- 2.3.2. Polygon object manipulation -- 2.4. Mapping Tools -- 2.4.1. llmap -- 2.4.2. tmap -- 2.4.3. spplot -- 2.4.4. ggplot2 -- 2.5. Chapter Appendix: llmap R Function Code -- 3. Bayesian Hierarchical Models -- 3.1. Likelihood Models -- 3.2. Prior Distributions -- 3.2.1. Propriety -- 3.2.2. Non-informative priors -- 3.3. Posterior Distributions -- 3.4. Bayesian Hierarchical Modeling -- 3.4.1. Hierarchical models -- 3.5. Posterior Inference -- 3.5.1. A Bernoulli and binomial example -- 3.5.2. Random e ects with a binomial example -- 4. Computation -- 4.1. Posterior Sampling -- 4.2. Markov Chain Monte Carlo Methods -- 4.2.1. Metropolis updates -- 4.2.2. Metropolis-Hastings updates -- 4.2.3. Gibbs updates -- 4.2.4. M-H versus Gibbs algorithms -- 4.2.5. Special methods -- 4.2.6. Convergence -- 4.2.7. Subsampling and thinning -- 4.3. Posterior and Likelihood Approximations -- 4.3.1. Pseudo-likelihood and other forms -- 4.3.2. Asymptotic approximations -- 5. Bayesian model Goodness of Fit Criteria -- 5.1. The Deviance Information Criterion -- 5.2. Watanabe AIC (WAIC) -- 5.3. Posterior Predictive Loss -- 5.4. Bayesian Residuals -- 5.5. Predictive Residuals and the Bootstrap -- 5.6. Conditional Predictive Ordinates (CPO) -- 5.7. Pseudo Bayes Factors and Marginal Predictive Likelihood -- 5.8. Exceedance Probabilities -- 6. Bayesian model Goodness of Fit Criteria -- 6.1. An Introduction to Case Event and Count Likelihoods -- 6.1.1. The Poisson process model -- 6.1.2. The conditional logistic model

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