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This week Dr. Green introduced us some basics of decision science. Decision science is a statistical model for decision making. I believe it can be used to solve various problems relating to the allocation of scarce resources subject to constraints. Dr. Green’s lecture focused on the utilization of decision science to healthcare. Dr. Green introduced us how to construct and analysis decision trees. The four important elements of decision tree are decision nodes, chance nodes, probabilities for each possible outcome of a chance event and utilities.

Another important aspect of applying decision science in diagnose is to test decisions. In this section, statistical terminologies are introduced to us. In diagnose, prior probability is easier to get than posterior probability. I think that’s why we apply Bayes’ rule to calculate posterior probability, using prior probability.


Posted by  Jing