detail regarding Exercise 7.15

We'll do Exercise 7.15 during Wed Aiug 27. There's a little mistake in the text (which will duly be repaired): I first write outcomes (4, 6, 6, 7, 8, 9), and later in the same exercise  (2, 5, 3, 7, 5, 8). This is of minor importance, for this toy example; the point is to build and run Dirichlet-simulation based algorithms, then read off the answers. It should be very easy to re-run your code, for any new dataset (and with the same priors). But let's stick to the (4, 6, 6, 7, 8, 9).

Point (b), with the delta method for the ML estimate of \delta, requires a bit of machinery from Ch 5 (or Ch 2), and I will spend a couple of minutes on that as well -- but the main Bayesian things, at this stage, is to understand the posterior, run simulations, and read off answers. 

The recipe via independent gammas of point (a) is perfectly fine & insightful. You may however also do library("MCMCpack") and then their "rdirichlet", to save you from a couple of coding lines.