Consider the program Seq08.exe from Chapter 8. First, execute the sequential minimum risk point estimation rule by fixing A = 100, c = 0.04, = 2,m = 10 and number of simulations = 1. The program will implement the purely sequential stopping rule from (8.2.5) one time only and upon termination, the screen output will show the values of n and b among other entries. Recall that this b is the sample mean so thatHaving observed n and t, now revisit Exercise 15.2.9 under a prior and evaluate the Bayes estimate of under a squared error loss function.

Exercise 15.2.9

Suppose that X₁,... ,X_n are i.i.d. with a common p.d.f. f(x; ) = 1 exp(x/)I(x > 0) given = where (> 0) is an unknown parameter. We referred to this distribution as Expo() in Chapter 8. Assume prior density

where (> 0) and (> 0) are known. This prior is called inverse gamma and it is referred to as IG(,) distribution.