Approximating the likelihood function
The first election poll is in! \(X\) = 6 of 10 polled voters plan to vote for you. You can use these data to build insight into your underlying support \(p\). To this end, you will use the likelihood_sim data frame (in your workspace). This contains the values of \(X\) (poll_result) simulated from each of 1,000 possible values of \(p\) between 0 to 1 (p_grid).
Deze oefening maakt deel uit van de cursus
Bayesian Modeling with RJAGS
Oefeninstructies
The ggplot() here constructs the distribution of \(p\) from which each possible outcome of \(X\) was generated. Modify this code, supplying a fill condition in order to highlight the distribution which corresponds to your observed poll_result, \(X=6\). This provides insight into which values of \(p\) are the most compatible with your observed poll data!
Note: do not wrap this condition in parentheses ().
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Density plots of p_grid grouped by poll_result
ggplot(likelihood_sim, aes(x = p_grid, y = poll_result, group = poll_result, fill = ___)) +
geom_density_ridges()