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Bayesian measures of model complexity and fit
The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages.
Hierarchical Modeling and Analysis for Spatial Data
OVERVIEW OF SPATIAL DATA PROBLEMS Introduction to Spatial Data and Models Fundamentals of Cartography Exercises BASICS OF POINT-REFERENCED DATA MODELS Elements of Point-Referenced Modeling Spatial…
Bayesian Model Choice Via Markov Chain Monte Carlo Methods
This paper presents a framework for Bayesian model choice, along with an MCMC algorithm that does not suffer from convergence difficulties, and applies equally well to problems where only one model is contemplated but its proper size is not known at the outset.
BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS
Approaches for Statistical Inference: The Bayes Approach, Model Criticism and Selection, and Performance of Bayes Procedures.
Markov Chain Monte Carlo conver-gence diagnostics: a comparative review
All of the methods in this work can fail to detect the sorts of convergence failure that they were designed to identify, so a combination of strategies aimed at evaluating and accelerating MCMC sampler convergence are recommended.
Markov Chain Monte Carlo in Practice: A Roundtable Discussion
Advice and guidance is offered to novice users of MCMC to help them build confidence in simulation results, methods for speeding and assessing convergence, estimating standard error, and more.
Hierarchical Bayesian Analysis of Changepoint Problems
A general approach to hierarchical Bayes changepoint models is presented, including an application to changing regressions, changing Poisson processes and changing Markov chains, which avoids sophisticated analytic and numerical high dimensional integration procedures.
Spatial Point Patterns
In spatial epidemiology, it is sought to find pattern in disease cases, perhaps different patterns for cases vs. controls, and in syndromic surveillance, to identify disease outbreaks, looking for clustering of cases.
Bayesian Methods for Data Analysis
Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior…
A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling
Abstract A solution to multivariate state-space modeling, forecasting, and smoothing is discussed. We allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation,…