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Sampling posteriors: The Gibbs sampler

The Gibbs sampler.

The need for sampling

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The Gibbs sampler

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Data 0.0, 0.8, 1.0, 1.2, 1.3, 1.3, 1.4, 1.8, 2.4, 4.6
Sample size \(N = 10\)
Sample mean \(\bar{y} = 1.58\) 
Sample variance \(s^2 = 1.51\)
Frequentist 95% CI for \(\mu\) \((0.7,  2.46)\)
Frequentist 95% CI for \(\sigma^2\) \((0.72, 5.04)\)

Full Conditional posterior distributions

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full conditionals

How the Gibbs sampler works



The Gibbs sampler.

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