1. Intro to Bayesian Hierarchical Modeling
P548: Bayesian Stats with Psych Applications Instructor: John Miyamoto 2/1/2017: Lecture 05-2
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2. Psych 548, Miyamoto, Aut ‘16

3. Kruschke Figure 9.7 (p. 236); Three Versions
Psych 548, Miyamoto, Win '17

4. Kruschke Figure 9.7 (p. 236); Three Versions
* Figures 1 and 2a use different notation to describe the same statistical model.
* Figures 2a and 2b
Psych 548, Miyamoto, Win '17

5. Psych 548, Miyamoto, Aut ‘16

6. Factorization of a Hierarchical Model (Kruschke, Eq. 9.11, p. 222)
Psych 548, Miyamoto, Win '17

7. Factorization of a Hierarchical Model (Kruschke, Eq. 9.11, p. 222)
Same
D is independent of w , conditioned on q.
Psych 548, Miyamoto, Win '17

8. Factorization of a Hierarchical Model (Kruschke, Eq. 9.11, p. 222)
D is independent of w , conditioned on q.
The diagram for a Bayesian graphical model displays the pattern of conditional independence and conditional dependence. In this case, theta "screens off" y from omega (analogous to Gopnik experiments with children's understanding of causal graphs).
Psych 548, Miyamoto, Win '17

9. Psych 548, Miyamoto, Aut ‘16

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Psych 548, Miyamoto, Aut ‘16