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avehtari · Oct 22, 2023 · c60165e · c60165e
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diff --git a/BDA3_notes.Rmd b/BDA3_notes.Rmd
@@ -712,10 +712,9 @@ $$
 
 - 5.1 Lead-in to hierarchical models
 - 5.2 Exchangeability (a useful theoretical concept)
-- 5.3 Bayesian analysis of hierarchical models
-- 5.4 Hierarchical normal model
-- 5.5 Example: parallel experiments in eight schools (uses
-    hierarchical normal model, skip the details of computation)
+- 5.3 Bayesian analysis of hierarchical models (discusses factorized computation which can be skipped)
+- 5.4 Hierarchical normal model (discusses factorized computation which can be skipped)
+- 5.5 Example: parallel experiments in eight schools (useful dicussion, skip the details of computation)
 - 5.6 Meta-analysis (can be skipped in this course)
 - 5.7 Weakly informative priors for hierarchical variance parameters
     (more recent discussion can be found in [Prior Choice Recommendation Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations))
@@ -917,7 +916,7 @@ be able to well define the exact tail shape of the prior, but
 half-normal produces usually more sensible prior predictive
 distributions and is thus better justified. Half-normal leads also
 usually to easier inference.
-
+ 
 See the [Prior Choice Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations)
 for more recent general discussion and model specific recommendations.
 

diff --git a/BDA3_notes.html b/BDA3_notes.html
@@ -1612,7 +1612,7 @@
 
 <h1 class="title toc-ignore">Bayesian Data Analysis course - BDA3
 notes</h1>
-<h4 class="date">Page updated: 2023-09-12</h4>
+<h4 class="date">Page updated: 2023-10-22</h4>
 
 </div>
 
@@ -2405,10 +2405,12 @@ <h3>Outline</h3>
 <ul>
 <li>5.1 Lead-in to hierarchical models</li>
 <li>5.2 Exchangeability (a useful theoretical concept)</li>
-<li>5.3 Bayesian analysis of hierarchical models</li>
-<li>5.4 Hierarchical normal model</li>
-<li>5.5 Example: parallel experiments in eight schools (uses
-hierarchical normal model, skip the details of computation)</li>
+<li>5.3 Bayesian analysis of hierarchical models (discusses factorized
+computation which can be skipped)</li>
+<li>5.4 Hierarchical normal model (discusses factorized computation
+which can be skipped)</li>
+<li>5.5 Example: parallel experiments in eight schools (useful
+dicussion, skip the details of computation)</li>
 <li>5.6 Meta-analysis (can be skipped in this course)</li>
 <li>5.7 Weakly informative priors for hierarchical variance parameters
 (more recent discussion can be found in <a href="https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations">Prior

diff --git a/slides/BDA_lecture_7.pdf b/slides/BDA_lecture_7.pdf