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| List of Figures | |
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| List of Tables | |
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| Preface | |
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| Acknowledgments | |
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| Introduction | |
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| Introducing Bayesian Analysis | |
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| The foundations of Bayesian inference | |
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| What is probability? | |
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| Probability in classical statistics | |
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| Subjective probability1 | |
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| Subjective probability in Bayesian statistics | |
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| Bayes theorem, discrete case | |
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| Bayes theorem, continuous parameter | |
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| Conjugate priors | |
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| Bayesian updating with irregular priors | |
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| Cromwell's Rule | |
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| Bayesian updating as information accumulation | |
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| Parameters as random variables, beliefs as distributions | |
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| Communicating the results of a Bayesian analysis | |
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| Bayesian point estimation | |
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| Credible regions | |
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| Asymptotic properties of posterior distributions | |
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| Bayesian hypothesis testing | |
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| Model choice | |
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| Bayes factors | |
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| From subjective beliefs to parameters and models | |
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| Exchangeability | |
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| Implications and extensions of de Finetti's Representation Theorem | |
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| Finite exchangeability | |
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| Exchangeability and prediction | |
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| Conditional exchangeability and multiparameter models | |
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| Exchangeability of parameters: hierarchical modeling | |
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| Historical note | |
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| Getting started: Bayesian analysis for simple models | |
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| Learning about probabilities, rates and proportions | |
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| Conjugate priors for probabilities, rates and proportions | |
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| Bayes estimates as weighted averages of priors and data | |
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| Parameterizations and priors | |
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| The variance of the posterior density | |
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| Associations between binary variables | |
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| Learning from counts | |
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| Predictive inference with count data | |
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| Learning about a normal mean and variance | |
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| Variance known | |
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| Mean and variance unknown | |
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| Conditionally conjugate prior | |
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| An improper, reference prior | |
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| Conflict between likelihood and prior | |
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| Non-conjugate priors | |
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| Regression models | |
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| Bayesian regression analysis | |
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| Likelihood function | |
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| Conjugate prior | |
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| Improper, reference prior | |
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| Further reading | |
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| Simulation Based Bayesian Analysis | |
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| Monte Carlo methods | |
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| Simulation consistency | |
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| Inference for functions of parameters | |
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| Marginalization via Monte Carlo integration | |
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| Sampling algorithms | |
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| Inverse-CDF method | |
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| Importance sampling | |
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| Accept-reject sampling | |
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| Adaptive rejection sampling | |
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| Further reading | |
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| Markov chains | |
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| Notation and definitions | |
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| State space | |
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| Transition kernel | |
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| Properties of Markov chains | |
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| Existence of a stationary distribution, discrete case | |
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| Existence of a stationary distribution, continuous case | |
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| Irreducibility | |
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| Recurrence | |
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| Invariant measure | |
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| Reversibility | |
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| Aperiodicity | |
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| Convergence of Markov chains | |
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| Speed of convergence | |
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| Limit theorems for Markov chains | |
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| Simulation inefficiency | |
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| Central limit theorems for Markov chains | |
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| Further reading | |
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| Markov chain Monte Carlo | |
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| Metropolis-Hastings algorithm | |
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| Theory for the Metropolis-Hastings algorithm | |
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| Choosing the proposal density | |
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| Gibbs sampling | |
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| Theory for the Gibbs sampler | |
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| Connection to the Metropolis algorithm | |
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| Deriving conditional densities for the Gibbs sampler: statistical models as conditional independence graphs | |
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| Pathologies | |
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| Data augmentation | |
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| Missing data problems | |
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| The slice sampler | |
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| Implementing Markov chain Monte Carlo | |
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| Software for Markov chain Monte Carlo | |
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| Assessing convergence and run-length | |
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| Working with BUGS/JAGS from R | |
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| Tricks of the trade | |
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| Thinning | |
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| Blocking | |
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| Reparameterization | |
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| Other examples | |
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| Further reading | |
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| Advanced Applications in the Social Sciences | |
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| Hierarchical Statistical Models | |
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| Data and parameters that vary by groups: the case for hierarchical modeling | |
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| Exchangeable parameters generate hierarchical models | |
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| �Borrowing strength� via exchangeability | |
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| Hierarchical modeling as a 'semi-pooling� estimator | |
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| Hierarchical modeling as a 'shrinkage� estimator | |
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| Computation via Markov chain Monte Carlo | |
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| ANOVA as a hierarchical model | |
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| One-way analysis of variance | |
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| Two-way ANOVA | |
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| Hierarchical models for longitudinal data | |
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| Hierarchical models for non-normal data | |
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| Multi-level models | |
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| Bayesian analysis of choice making | |
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| Regression models for binary responses | |
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| Probit model via data augmentation | |
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| Probit model via marginal data augmentation | |
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| Logit model | |
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| Binomial model for grouped binary data | |
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| Ordered outcomes | |
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| Identification | |
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| Multinomial outcomes | |
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| Multinomial logit (MNL) | |
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| Independence of irrelevant alternatives | |
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| Multinomial probit | |
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| Bayesian analysis via MCMC | |
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| Bayesian approaches to measurement | |
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| Bayesian inference for latent states | |
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| A formal role for prior information | |
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| Inference for many parameters | |
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| Factor analysis | |
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| Likelihood and prior densities | |
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| Identification | |
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| Posterior density | |
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| Inference over rank orderings of the latent variable | |
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| Incorporating additional information via hierarchical modeling | |
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| Item-response models | |
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| Dynamic measurement models | |
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| State-space models for [pooling the polls] | |
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| Bayesian inference | |
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| Appendices | |
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| Working with vectors and matrices | |
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| Probability review | |
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| Foundations of probability | |
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| Probability densities and mass functions | |
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| Probability mass functions for discrete random quantities | |
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| Probability density functions for continuous random quantities | |
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| Convergence of sequences of random variables | |
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| Proofs of selected propositions | |
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| Products of normal densities | |
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| Conjugate analysis of normal data | |
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| Asymptotic normality of the posterior density | |
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| References | |
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| Topic index | |
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| Author index | |