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Preface | p. xiii |
Introduction: Probability and parameters | p. 1 |
Probability | p. 1 |
Probability distributions | p. 5 |
Calculating properties of probability distributions | p. 7 |
Monte Carlo integration | p. 8 |
Monte Carlo simulations using BUGS | p. 13 |
Introduction to BUGS | p. 13 |
Background | p. 13 |
Directed graphical models | p. 13 |
The BUGS language | p. 15 |
Running BUGS models | p. 16 |
Running WinBUGS for a simple example | p. 17 |
DoodleBUGS | p. 21 |
Using BUGS to simulate from distributions | p. 22 |
Transformations of random variables | p. 24 |
Complex calculations using Monte Carlo | p. 26 |
Multivariate Monte Carlo analysis | p. 27 |
Predictions with unknown parameters | p. 29 |
Introduction to Bayesian inference | p. 33 |
Bayesian learning | p. 33 |
Bayes' theorem for observable quantities | p. 33 |
Bayesian inference for parameters | p. 34 |
Posterior predictive distributions | p. 36 |
Conjugate Bayesian inference | p. 36 |
Binomial data | p. 37 |
Normal data with unknown mean, known variance | p. 41 |
Inference about a discrete parameter | p. 45 |
Combinations of conjugate analyses | p. 49 |
Bayesian and classical methods | p. 51 |
Likelihood-based inference | p. 52 |
Exchangeability | p. 52 |
Long-run properties of Bayesian methods | p. 53 |
Model-based vs procedural methods | p. 54 |
The "likelihood principle" | p. 55 |
Introduction to Markov chain Monte Carlo methods | p. 57 |
Bayesian computation | p. 57 |
Single-parameter models | p. 57 |
Multi-parameter models | p. 59 |
Monte Carlo integration for evaluating posterior integrals | p. 61 |
Markov chain Monte Carlo methods | p. 62 |
Gibbs sampling | p. 63 |
Gibbs sampling and directed graphical models | p. 64 |
Derivation of full conditional distributions in BUGS | p. 68 |
Other MCMC methods | p. 68 |
Initial values | p. 70 |
Convergence | p. 71 |
Detecting convergence/stationarity by eye | p. 72 |
Formal detection of convergence/stationarity | p. 73 |
Efficiency and accuracy | p. 77 |
Monte Carlo standard error of the posterior mean | p. 77 |
Accuracy of the whole posterior | p. 78 |
Beyond MCMC | p. 79 |
Prior distributions | p. 81 |
Different purpose of priors | p. 81 |
Vague, "objective," and "reference" priors | p. 82 |
Introduction | p. 82 |
Discrete uniform distributions | p. 83 |
Continuous uniform distributions and Jeffreys prior | p. 83 |
Location parameters | p. 84 |
Proportions | p. 84 |
Counts and rates | p. 85 |
Scale parameters | p. 87 |
Distributions on the positive integers | p. 88 |
More complex situations | p. 89 |
Representation of informative priors | p. 89 |
Elicitation of pure judgement | p. 90 |
Discounting previous data | p. 93 |
Mixture of prior distributions | p. 95 |
Sensitivity analysis | p. 97 |
Regression models | p. 103 |
Linear regression with normal errors | p. 103 |
Linear regression with non-normal errors | p. 107 |
Non-linear regression with normal errors | p. 109 |
Multivariate responses | p. 112 |
Generalised linear regression models | p. 114 |
Inference on functions of parameters | p. 118 |
Further reading | p. 119 |
Categorical data | p. 121 |
2 X 2 tables | p. 121 |
Tables with one margin fixed | p. 122 |
Case-control studies | p. 125 |
Tables with both margins fixed | p. 126 |
Multinomial models | p. 126 |
Conjugate analysis | p. 126 |
Non-conjugate analysis-parameter constraints | p. 128 |
Categorical data with covariates | p. 129 |
Multinomial and Poisson regression equivalence | p. 131 |
Contingency tables | p. 132 |
Ordinal regression | p. 132 |
Further reading | p. 134 |
Model checking and comparison | p. 137 |
Introduction | p. 137 |
Deviance | p. 138 |
Residuals | p. 140 |
Standardised Pearson residuals | p. 140 |
Multivariate residuals | p. 142 |
Observed p-values for distributional shape | p. 143 |
Deviance residuals and tests of fit | p. 145 |
Predictive checks and Bayesian p-values | p. 147 |
Interpreting discrepancy statistics - how big is big? | p. 147 |
Out-of-sample prediction | p. 148 |
Checking functions based on data alone | p. 148 |
Checking functions based on data and parameters | p. 152 |
Goodness of fit for grouped data | p. 155 |
Model assessment by embedding in larger models | p. 157 |
Model comparison using deviances | p. 159 |
pD: The effective number of parameters | p. 159 |
Issues with pD | p. 161 |
Alternative measures of the effective number of parameters | p. 164 |
DIC for model comparison | p. 165 |
How and why does WinBUGS partition DIC and pD? | p. 167 |
Alternatives to DIC | p. 168 |
Bayes factors | p. 169 |
Lindley-Bartlett paradox in model selection | p. 171 |
Computing marginal likelihoods | p. 172 |
Model uncertainty | p. 173 |
Bayesian model averaging | p. 173 |
MCMC sampling over a space of models | p. 173 |
Model averaging when all models are wrong | p. 175 |
Model expansion | p. 176 |
Discussion on model comparison | p. 177 |
Prior-data conflict | p. 178 |
Identification of prior-data conflict | p. 179 |
Accommodation of prior-data conflict | p. 180 |
Issues in Modelling | p. 185 |
Missing data | p. 185 |
Missing response data | p. 186 |
Missing covariate data | p. 189 |
Prediction | p. 193 |
Measurement error | p. 195 |
Cutting feedback | p. 201 |
New distributions | p. 204 |
Specifying a new sampling distribution | p. 204 |
Specifying a new prior distribution | p. 205 |
Censored, truncated, and grouped observations | p. 206 |
Censored observations | p. 206 |
Truncated sampling distributions | p. 208 |
Grouped, rounded, or interval-censored data | p. 209 |
Constrained parameters | p. 211 |
Univariate fully specified prior distributions | p. 211 |
Multivariate fully specified prior distributions | p. 211 |
Prior distributions with unknown parameters | p. 214 |
Bootstrapping | p. 214 |
Ranking | p. 215 |
Hierarchical models | p. 219 |
Exchangeability | p. 219 |
Priors | p. 223 |
Unit-specific parameters | p. 223 |
Parameter constraints | p. 223 |
Priors for variance components | p. 225 |
Hierarchical regression models | p. 227 |
Data formatting | p. 230 |
Hierarchical models for variances | p. 237 |
Redundant parameterisations | p. 240 |
More general formulations | p. 242 |
Checking of hierarchical models | p. 242 |
Comparison of hierarchical models | p. 249 |
"Focus": The crucial element of model comparison in hierarchical models | p. 250 |
Further resources | p. 252 |
Specialised models | p. 253 |
Time-to-event data | p. 253 |
Parametric survival regression | p. 254 |
Time series models | p. 257 |
Spatial models | p. 262 |
Intrinsic conditionally autoregressive (CAR) models | p. 263 |
Supplying map polygon data to WinBUGS and creating adjacency matrices | p. 264 |
Multivariate CAR models | p. 268 |
Proper CAR model | p. 269 |
Poisson-gamma moving average models | p. 269 |
Geostatistical models | p. 270 |
Evidence synthesis | p. 273 |
Meta-analysis | p. 273 |
Generalised evidence synthesis | p. 274 |
Differential equation and pharmacokinetic models | p. 278 |
Finite mixture and latent class models | p. 280 |
Mixture models using an explicit likelihood | p. 283 |
Piecewise parametric models | p. 286 |
Change-point models | p. 286 |
Splines | p. 288 |
Semiparametric survival models | p. 288 |
Bayesian nonparametric models | p. 291 |
Dirichlet process mixtures | p. 293 |
Stick-breaking implementation | p. 293 |
Different implementations of BUGS | p. 297 |
Introduction-BUGS engines and interfaces | p. 297 |
Expert systems and MCMC methods | p. 298 |
Classic BUGS | p. 299 |
WinBUGS | p. 300 |
Using WinBUGS: compound documents | p. 301 |
Formatting data | p. 301 |
Using the WinBUGS graphical interface | p. 304 |
Doodles | p. 308 |
Scripting | p. 308 |
Interfaces with other software | p. 310 |
R2WinBUGS | p. 311 |
WBDev | p. 313 |
OpenBUGS | p. 315 |
Differences from WinBUGs | p. 317 |
OpenBUGS on Linux | p. 317 |
BRugs | p. 318 |
Parallel computation | p. 319 |
JAGS | p. 320 |
Extensibility: modules | p. 321 |
Language differences | p. 321 |
Other differences from WinBUGS | p. 324 |
Running JAGS from the command line | p. 325 |
Running JAGS from R | p. 326 |
BUGS language syntax | p. 329 |
Introduction | p. 329 |
Distributions | p. 329 |
Standard distributions | p. 329 |
Censoring and truncation | p. 330 |
Non-standard distributions | p. 331 |
Deterministic functions | p. 331 |
Standard functions | p. 331 |
Special functions | p. 331 |
Add-on functions | p. 332 |
Repetition | p. 332 |
Multivariate quantities | p. 333 |
Indexing | p. 334 |
Functions as indices | p. 334 |
Implicit indexing | p. 334 |
Nested indexing | p. 334 |
Data transformations | p. 335 |
Commenting | p. 335 |
Functions in BUGS | p. 337 |
Standard functions | p. 337 |
Trigonometric functions | p. 337 |
Matrix algebra | p. 337 |
Distribution utilities and model checking | p. 340 |
Functionals and differential equations | p. 341 |
Miscellaneous | p. 342 |
Distributions in BUGS | p. 343 |
Continuous univariate, unrestricted range | p. 343 |
Continuous univariate, restricted to be positive | p. 345 |
Continuous univariate, restricted to a finite interval | p. 349 |
Continuous multivariate distributions | p. 350 |
Discrete univariate distributions | p. 351 |
Discrete multivariate distributions | p. 354 |
Bibliography | p. 357 |
Index | p. 373 |
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The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.