1. 1 Revising Judgments in the Light of New Information

2. 2 Bayes’ theorem Prior probability New information Posterior probability

3. 3 The components problem

4. 4 Applying Bayes’ theorem to the components problem

5. 5 Vague priors and very reliable information

6. 6 The effect of the reliability of information on the modification of prior probabilities

7. 7 The retailer’s problem with prior probabilities

8. 8 Applying Bayes’ theorem to the retailer’s problem

9. 9 Applying posterior probabilities to the retailer’s problem

10. 10 Determining the EVPI

11. 11 Calculating the EVPI

12. 12 Deciding whether to buy imperfect information

13. 13 If test indicates virus is present

14. 14 If test indicates virus is absent

15. 15 Determining the EVII

16. 16 Expected profit with imperfect information = $62 155 Expected profit without the information = $57 000 Expected value of imperfect information (EVII) = $5 155