| Description |
1 online resource (485 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- 1 Introduction: Experiments and variables -- 1.1 Chapter pre-cap -- 1.2 Experiments and effects -- 1.2.1 Experiments and inference -- 1.3 Our experiment -- 1.3.1 Our experiment: Introduction -- 1.3.2 Our experimental methods -- 1.3.3 Our research questions -- 1.3.4 Our experimental data -- 1.4 Variables -- 1.4.1 Populations and samples -- 1.4.2 Dependent and independent variables -- 1.4.3 Categorical variables and 'factors' -- 1.4.4 Quantitative variables |
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1.5 Inspecting our data -- 1.5.1 Inspecting categorical variables -- 1.5.2 Inspecting quantitative variables -- 1.6 Exercises -- Reference -- 2 Probabilities, likelihood, and inference -- 2.1 Chapter pre-cap -- 2.2 Data and research questions -- 2.3 Empirical probabilities -- 2.3.1 Conditional and marginal probabilities -- 2.3.2 Joint probabilities -- 2.4 Probability distributions -- 2.5 The normal distribution -- 2.5.1 The sample mean -- 2.5.2 The sample variance (or standard deviation) -- 2.5.3 The normal density -- 2.5.4 The standard normal distribution -- 2.6 Models and inference |
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2.7 Probabilities of events and likelihoods of parameters -- 2.7.1 Characteristics of likelihoods -- 2.7.2 A brief aside on logarithms -- 2.7.3 Characteristics of likelihoods, continued -- 2.8 Answering our research questions -- 2.9 Exercises -- References -- 3 Fitting Bayesian regression models with brms -- 3.1 Chapter pre-cap -- 3.2 What are regression models? -- 3.3 What's 'Bayesian' about these models? -- 3.3.1 Prior probabilities -- 3.3.2 Posterior distributions -- 3.3.3 Posterior distributions and shrinkage -- 3.4 Sampling from the posterior using Stan and brms |
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3.5 Estimating a single mean with the brms package -- 3.5.1 Data and research questions -- 3.5.2 Description of the model -- 3.5.3 Errors and residuals -- 3.5.4 The model formula -- 3.5.5 Fitting the model: Calling the brm function -- 3.5.6 Interpreting the model: The print statement -- 3.5.7 Seeing the samples -- 3.5.8 Getting the residuals -- 3.6 Checking model convergence -- 3.7 Specifying prior probabilities -- 3.8 The log prior and log posterior densities -- 3.9 Answering our research questions -- 3.10 'Traditionalists' corner -- 3.10.1 One-sample t-test vs. intercept-only Bayesian models |
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3.10.2 Intercept-only ordinary-least-squares regression vs. intercept-only Bayesian models -- 3.11 Exercises -- 4 Inspecting a 'single group' of observations using a Bayesian multilevel model -- 4.1 Chapter pre-cap -- 4.2 Repeated measures data -- 4.2.1 Multilevel models and 'levels' of variation -- 4.3 Representing predictors with many levels -- 4.4 Strategies for estimating factors with many levels -- 4.4.1 Complete pooling -- 4.4.2 No pooling -- 4.4.3 (Adaptive) Partial pooling -- 4.4.4 Hyperpriors -- 4.5 Estimating a multilevel model with brms -- 4.5.1 Data and research questions |
| Notes |
Description based upon print version of record |
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4.5.2 Description of the model |
| Subject |
Bayesian statistical decision theory.
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R (Computer program language)
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Multilevel models (Statistics)
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| Form |
Electronic book
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| Author |
Silbert, Noah
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| ISBN |
9781000869835 |
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1000869830 |
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