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Unit 8: Statistical Reasoning




              Conditional probability, P(A|B), signifies the probability of event A  specified that we  Notes
               know event B has appeared.

                                               P(E|H )P(H )
              Bayes  Theorem,  that  is  P(H |E)   i   i     signifies  that specified  some
                                       i       n
                                               k 1 P(E|H )P(H )
                                                           k
                                                      k
                                               
               evidence E  then probability  that hypothesis  is true  is equal  to the proportion of  the
               probability that E will be true specified H  times the a priori evidence on the probability
                                                 i
               of H and the sum of the probability of E over the set of all hypotheses times the probability
                  i
               of these hypotheses.
              Bayesian statistics lounge at the heart of many statistical reasoning systems.
              Bayesian networks are also known as Belief Networks or Probabilistic Inference Networks
               primarily generated by Pearl (1988).
              Certainty Factors  do hold on to the rules of Bayesian  statistics, but it can symbolize
               tractable knowledge systems.
              Fuzzy logic concentrates on ambiguities in illustrating events  rather than uncertainty
               regarding the incidence of an event.
              Fuzzy set theory defines set membership as a possibility distribution.

          8.6 Keywords

          Bayesian networks: Bayesian networks are also known as Belief  Networks or Probabilistic
          Inference Networks primarily generated by Pearl (1988).
          Fuzzy Logic: Fuzzy logic concentrates on ambiguities in illustrating events rather than uncertainty
          regarding the incidence of an event.
          Fuzzy Set: Fuzzy set theory defines set membership as a possibility distribution.
          Probabilities: Probabilities are (real) numbers in the range 0 to 1.

          8.7 Review Questions

          1.   What is Bayes Theorem? Also illustrate Bayes rule-based systems.

          2.   Enlighten the probabilistic reasoning aspects of MYCIN.
          3.   Given that the first card dealt from a pack of cards was ace what is the probability that the
               next card will be an ace?

          4.   What are Bayesian networks? Illustrate the concept of implementation with example.
          5.   Given that
               (a)  the probability that it will rain in Cardiff tomorrow is 0.8

               (b)  the probability that there are Sea Gulls on Roath Park lake given that it will rain
                    tomorrow is 0.1
               (c)  the probability that there are Sea Gulls on Roath Park lake given that it will not rain
                    tomorrow is 0.05
               What is the probability that it will rain tomorrow provided that there a Sea Gulls on Roath
               Park lake?






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