Conditioning on the data (again)

Let's resurrect the zombie site example where you tested text ads. Out of a 100 impressions of the text ad, 6 out of a 100 clicked and visited your site.

To the right is roughly the code you developed in the last exercise. pars is currently the joint distribution over all combinations of proportion_clicks and n_visitors.

This exercise is part of the course

Fundamentals of Bayesian Data Analysis in R

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Exercise instructions

Condition on the data and keep only the rows in pars where n_visitors == 6.
Normalize pars$probability again, to make sure it sums to 1.0.
Plot the posterior pars$probability using plot(x = , y = , type = "h") with pars$proportion_clicks on the x-axis and pars$probability on the y-axis.

Hands-on interactive exercise

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

n_ads_shown <- 100
proportion_clicks <- seq(0, 1, by = 0.01)
n_visitors <- seq(0, 100, by = 1)
pars <- expand.grid(proportion_clicks = proportion_clicks,
                    n_visitors = n_visitors)
pars$prior <- dunif(pars$proportion_clicks, min = 0, max = 0.2)
pars$likelihood <- dbinom(pars$n_visitors, 
    size = n_ads_shown, prob = pars$proportion_clicks)
pars$probability <- pars$likelihood * pars$prior
pars$probability <- pars$probability / sum(pars$probability)
# Condition on the data 
pars <- ___
# Normalize again
pars$probability <- ___
# Plot the posterior pars$probability
plot(___)

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