1 Carry out a full decision analysis for Classical Reproductions Ltd, using the following information

Prior probabilities for the various events for the next 12 months are:
(A) = 0.3 (B) = 0.4 (C) = 0.3 Background to the application of Bayesian decision theory It has been mentioned throughout the chapter that since the 1960s we have seen the development of sophisticated statistical techniques for problem-solving where information is incomplete or uncertain.
The new area of statistics has a variety of names – statistical decision theory, simple decision theory and Bayesian decision theory (after the Reverend Thomas Bayes (1702–61)). These names can be used interchangeably, but for the purposes of this case we use the term Bayesian decision theory.
Bayesian decision theory is a relatively new and somewhat controversial method for dealing with future uncertainties. Applied to forecasting, the technique incorporates the firm’s own guesses as data inputs into the calculation of a sales forecast. There are essentially two ways of conceiving probability:
● as a physical property, inherent to a physical system;
● as a measure of belief in the truth of some statement.

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Calculation of expected profit with perfect information Event A B 1C Profit for optimal act 800,000 450,000 80,000 Probability 0.4 0.3 0.3 Expected value () 320,000 135,000 24,000 479,000