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Bayesian Decision Theory

Bayesian Decision Theory is a particularly useful scheme for suture truing the research decision so that the proper choice of alternative courses of action results after the outcome of each alternative is nu-metrically evaluated. The decision maker under the Baye’s Theorem quantifies “his fallings about uncertainty in terms of subjectively as sassed numerical probabilities, even when there is no past history on which to base the assessment of probabilities.” Futher when the research is the indicated choice. Bayesian analysis allows a systematic investigation of alternative strategies as to their potential value Bayesian Decision Theory can be employed to determine whether research should be conducted or whether the decision should be made with the available information without additional research Bayesian Approach can be compared with the uncertainty elimination in the case of tossing a coin and expecting 50 per cent heads and tails each. Also when the coin used in unfair, judgment about the unfairness to certain extent is to be made. In Bayesian Approach, it is done with the application of subjective or personal probabilities to decision making and their probability outcomes. Bayesian Approach holds that implicit probabilities should be made explicit.

(1)    There is an individual (I) operating within some environment (n) to whom the problem can be attributed.

(2)    The individual has at least two courses of action (A1 and  A2) which he can pursue and is able to make a choice from the available courses of action.

(3)    There are at least two possible outcomes (O1 and O2) of his choice, and he prefers one of these outcomes to the others. In other words, there must be at lest one outcome which he wants. There must be an objective.

(4)    The courses of action available to him, provide some choice but not an equal chance of getting the desired outcome. Since the chances are equal, the choice would not matter.

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