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Doing Bayesian Data Analysis by John Kruschke

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Chapter 4

Bayes’ Rule

Contents

4.1 Bayes’ Rule

4.1.1 Derived from Definitions of Conditional Probability

4.1.2 Intuited from a Two-Way Discrete Table

4.1.3 The Denominator as an Integral over Continuous Values

4.2 Applied to Models and Data

4.2.1 Data Order Invariance

4.2.2 An Example with Coin Flipping

4.3 The Three Goals of Inference

4.3.1 Estimation of Parameter Values

4.3.2 Prediction of Data Values

4.3.3 Model Comparison

4.3.4 Why Bayesian Inference Can Be Difficult

4.3.5 Bayesian Reasoning in Everyday Life

4.4 R Code

4.4.1 R Code for Figure 4.1

4.5 Exercises

I’ll love you forever in every respect

(I’ll marginalize all your glaring defects)

But if you could change some to be more like me

I’d love you today unconditionally.

If you see that ...

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