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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).

This exercise is part of the course

Bayesian Modeling with RJAGS

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Exercise instructions

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 ().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# 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()
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