HW1 bayesian

3.7 Posterior prediction: Consider a pilot study in which m = 15 children enrolled in special education classes were randomly selected and tested for a certain type of learning disability. In the pilot study, 3/1 = 2 children tested positive for the disability. a) Using a uniform prior distribution, nd the posterior distribution of 9, the fraction of students in special education classes who have the disability. Find the posterior mean, mode and standard deviation of 6, and plot the posterior density. Researchers would like to recruit students With the disability to partici- pate in a long-term study, but rst they need to make sure they can recruit enough students. Let M = 278 be the number of children in special edu- cation classes in this particular school district, and let Yg be the number of students with the disability. b) Find Pr(Y2 = y2|Y1 = 2), the posterior predictive distribution of Y2, as follows: i. Discuss What assumptions are needed about the joint distribution of (Y1, Y2) such that the following is true: 1 PI'(Y2 = yle1 = 2) = / Pr(Y2 = y2|9)p(9|Y1 = 2) d9. 0 ii. Now plug in the forms for Pr(Y2 = y2|9) and p(0|Y1 = 2) in the above integral. iii. Figure out What the above integral must be by using the calculus result discussed in Section 3.1. c) Plot the function Pr(Y2 = y2|Y1 = 2) as a function of y2. Obtain the mean and standard deviation of Y2, given Y1 = 2. d) The posterior mode and the MLE (maximum likelihood estimate; see Exercise 3.14) of 6, based on data from the pilot study, are both 2/15 Plot the distribution Pr(Y2 y2|6= 9), and nd the mean and stande deviation of Y2 given 3 0. Compare these results to the plots and calculations in c) and discuss any differences. Which distribution for Y2 would you use to make predictions, and Why