CoffeeTime is considering selling juices along with its other products.

                            States of Nature
                      High Sales         Med. Sales       Low Sales
                       A(0.2)               B(0.5)           C(0.3)

A1 (sell juice)        3000                  2000           -6000
A2 (don't sell juices) 0                     0              0

The probabilities shown above represent the states of nature and the
decision maker's (e.g. manager) degree of uncertainties and personal
judgement on the occurrence of each state. What is the expected payoff
for actions A1 and A2 above? What would be your recommendation?
Interpret the results based on practical considerations.

Using Bayes and empirical Bayes (EB) methods that combine information
similar components of information and produce efficient inferences for
both individual components and shared model characteristics, how could
CoffeeTime "borrow information" from adjacent cities or other counties
to employ Bayesian logic?