From 9ff44e5ed722c2d7c4cb9487aa5464dcdeeb78cd Mon Sep 17 00:00:00 2001 From: Ville Laitinen Date: Fri, 28 Jun 2024 20:25:50 +0300 Subject: [PATCH] sdf --- episodes/exercises.Rmd | 4 ++-- episodes/other-topics.Rmd | 12 +++--------- 2 files changed, 5 insertions(+), 11 deletions(-) diff --git a/episodes/exercises.Rmd b/episodes/exercises.Rmd index c917c1f3..889eefc0 100644 --- a/episodes/exercises.Rmd +++ b/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. diff --git a/episodes/other-topics.Rmd b/episodes/other-topics.Rmd index 3946c56b..fd074b39 100644 --- a/episodes/other-topics.Rmd +++ b/episodes/other-topics.Rmd @@ -24,19 +24,19 @@ posterior_color <- "#56B4E9" :::::::::::::::::::::::::::::::::::::: questions -- What next? +- Motivating questions :::::::::::::::::::::::::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::: objectives -- Learn more +- Learn to use Stan with additional R packages :::::::::::::::::::::::::::::::::::::::::::::::: -Add text +Add text here ::::::::::::::::::::::::::::::::::::::::: challenge @@ -56,12 +56,6 @@ More text -::::::::::::::::::::::::::::::::::::: discussion - -Discussion poitns - -::::::::::::::::::::::::::::::::::::::::::::::: - ::::::::::::::::::::::::::::::::::::: keypoints