Suppose y1, . . . , yn iid PoissonGamma(a, b) p(y | a, b) = (y + a) (a)y! ba (b + 1)y+a with a, b > 0. This density is an appropriate model for overdisperse counts (i.e., that show more dispersion than the predicted under the Poisson model). Suppose the non informative prior p(a, b) 1/(ab)2. If we transform the real-valued parameters 1 = log a and 2 = log b, the posterior density is proportional to p(1, 2 | data) 1 ab Yn i=1 (yi + a) (a)yi! ba (b + 1)yi+a where a = exp 1 and b = exp 2. Use this framework to model data collected by Gilchrist (1984), in which a series of 33 insect traps were set across sand dines and a number of different insects caught over a fixed time were recorded. The number of insects of the taxa Staphylinoidea caught in the traps