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Sampling-Based Approaches to Calculating Marginal Densities
Abstract Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to theExpand
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 SpatialExpand
Bayesian Model Choice: Asymptotics and Exact Calculations
Abstract : Model determination is a fundamental data analytic task. Here we consider the problem of choosing amongst a finite (with loss of generality we assume two) set of models. After brieflyExpand
Gaussian predictive process models for large spatial data sets.
This work achieves the flexibility to accommodate non-stationary, non-Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets in the form of a computational template encompassing these diverse settings. Expand
Model choice: A minimum posterior predictive loss approach
A predictive criterion where the goal is good prediction of a replicate of the observed data but tempered by fidelity to the observed values is proposed, which is obtained by minimising posterior loss for a given model. Expand
Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling
Abstract The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, includingExpand
Model Determination Using Predictive Distributions with Implementation via Sampling-Based Methods
Model determination is divided into the issues of model adequacy and model selection and it is proposed to validate conditional predictive distributions arising from single point deletion against observed responses. Expand
Hierarchical Bayesian Analysis of Changepoint Problems
SUMMARY A general approach to hierarchical Bayes changepoint models is presented. In particular, desired marginal posterior densities are obtained utilizing the Gibbs sampler, an iterative MonteExpand
Bayesian statistics without tears: A sampling-resampling perspective
A straightforward sampling-resampling perspective on Bayesian inference is offered, which has both pedagogic appeal and suggests easily implemented calculation strategies. Expand