The BUGS Project
The BUGS Project
version 0.5 manual
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Contents
1 Introduction
1.1 What is BUGS?
1.2 For what kind of problems is BUGS best suited?
1.3 Markov Chain Monte Carlo (MCMC) techniques
1.4 A simple example
1.5 Hardware platforms
1.6 Software
1.7 Format of manual
1.8 Referring to BUGS
1.9 Changes since Version 0.30
2 Getting started
2.1 Obtaining BUGS and CODA
2.2 Specific platforms
2.2.1 Sparc Stations
2.2.2 HP's
2.2.3 PCs
2.3 Using BUGS with S-plus
2.4 Testing the installation
3 Specifying your model
3.1 Directed graphical models
3.2 Restrictions to log-concave distributions
4 Expressing your model in the BUGS language
4.1 Lexical conventions
4.2 Declarations
4.3 Model description
4.4 Distributions
4.5 Using multivariate nodes
4.6 Bounds on the domain of variables
4.7 Functions
4.8 Modelling functions of parameters
4.9 Log-concave priors
4.10 Indexing
4.10.1 Functions as an index
4.10.2 Implicit indexing
4.10.3 Nested indexing
4.11 for loops
4.12 Functions of variables
4.13 Data transformations within BUGS
5 Getting your data into BUGS: data and initial value files
5.1 Data and initial value files
5.2 Forward sampling to generate initial values
5.3 File format for data and initial value files: rectangular format
5.4 File format for data and initial value files: S-plus format
5.5 Variable-length records (ragged arrays)
5.6 Missing data
5.7 Random number seed
5.8 Preparation of data using statistical packages
6 Running BUGS
6.1 BUGS commands
6.2 Running BUGS in the background
6.3 Example
6.4 Errors when running BUGS
7 Processing output
7.1 Main output
7.2 Output of summary statistics
7.3 Convergence: using the diag command
7.4 Convergence: using the out command
8 Errors
8.1 Errors found by BUGS
8.2 Errors found by the operating system
8.3 Undetected errors
9 Topics in modelling
9.1 General Strategy
9.2 `Non-informative priors'
9.2.1 Issues with improper priors
9.2.2 Pareto priors for precision parameters
9.2.3 Assessment of proper priors for precision parameters
9.3 Model criticism and selection
9.3.1 Checking through examination of individual observations.
9.3.2 Comparison between two or more candidate models
9.3.3 Global goodness-of-fit tests based on Bayesian p-values
9.4 Implementation of selected model checking criteria in BUGS
9.5 Ranking
9.6 Measurement error
9.7 Multinomial-Poisson transformation
9.7.1 Multinomial-logistic models
9.7.2 Conditional likelihoods in case-control studies
9.7.3 Partial likelihoods in Cox regression models
9.8 Logical constraints
9.9 Parameterisation
9.10 Undirected links, chain graphs and neighbourhood models
9.11 Multivariate normal nodes
9.11.1 Use of Multivariate normal observations
9.11.2 Use of Multivariate normal parameters
9.11.3 Use of the Wishart distribution
9.11.4 Avoiding multivariate nodes for parameters
Acknowledgements
References
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