DataCamp

Poisson posterior prediction

Your l_weekend and l_weekday variables reflect the trends in volume on 80 degree weekends and weekdays:

> head(poisson_chains, 2)
       a b.1.    b.2.      c l_weekend l_weekday
1 5.0198    0 -0.1222 0.0141   465.924   412.324
2 5.0186    0 -0.1218 0.0141   466.284   412.829

Now that you understand the trends, let's make some predictions! Specifically, let's predict trail volumes on the next 80 degree weekend and 80 degree weekday. To do so, you must take into account individual variability from the trend, modeled by the likelihood \(Y\)_i \(\sim Pois(l\)_i).

Using rpois(n, lambda) for sample size n and rate parameter lambda, you will simulate Poisson predictions of volume under each set of posterior plausible parameters in poisson_chains.

For each of the 10,000 sets of parameter values in poisson_chains, use rpois() to predict volume on an 80 degree weekend and 80 degree weekday. Store these as Y_weekend and Y_weekday in poisson_chains.
Print the first 6 rows of poisson_chains.
Use ggplot() to construct a density plot of your Y_weekday predictions.
Approximate the posterior probability that the volume on an 80 degree weekday is less than 400 users.

Take Hint (-30 XP)

Exercise

Poisson posterior prediction

Instructions