sdf

episodes/exercises.Rmd

@@ -235,7 +235,7 @@ Plot the posterior distribution and color the points according to the condition
 
 The AR(1) process is defined by the recursion: 
 $$x_i \sim N(\phi \cdot x_{i-1}, \sigma^2),$$
-where $i$ is a time index. In other words, given some initial value $x_1$, the next value $x_2$ is generated from a normal distribution with mean $\phi\cdot x_1$ and variance $sigma^2$. This pattern continues for the successive values.
+where $i$ is a time index. In other words, given some initial value $x_1$, the next value $x_2$ is generated from a normal distribution with mean $\phi\cdot x_1$ and variance $\sigma^2$. This pattern continues for the successive values.
 
 Write a Stan program that estimates the parameters $\phi$ and $\sigma$ of the AR(1) process. 
 
@@ -359,7 +359,7 @@ $$
 
 Use data 
 ```{r}
-y <-  c(2,7,4,3,5,2,7,5,5,5).
+y <-  c(2,7,4,3,5,2,7,5,5,5)
 ```
 
 Implement the model in Stan. 

episodes/other-topics.Rmd

@@ -24,19 +24,19 @@ posterior_color <- "#56B4E9"
 
 :::::::::::::::::::::::::::::::::::::: questions
 
-- What next?
+- Motivating questions
 
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 ::::::::::::::::::::::::::::::::::::: objectives
 
 
-- Learn more
+- Learn to use Stan with additional R packages
 
 
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-Add text
+Add text here
 
 
 ::::::::::::::::::::::::::::::::::::::::: challenge
@@ -56,12 +56,6 @@ More text
 
 
 
-::::::::::::::::::::::::::::::::::::: discussion
-
-Discussion poitns
-
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-
 
 
 ::::::::::::::::::::::::::::::::::::: keypoints