MA375: Lecture Notes

Fall 2008, Prof. Walther

Some Definitions

If E and F are events in S (sample space) the the conditional probability of E and F is P(E|F) = P(E intersect F).

Further :

         the conditional probability of "E" given "F" is =$  \frac {P(EnF)}{P(F)} $

defn: if P(E|F) = P(E) , then E and F are independent events otherwise they are dependant events.

           note: independence implies that  $  P(E)= P(E|F) = \frac {P(EnF)}{P(F)} $
                      or P(E).P(F)=P(EnF).
           note : if P(E|F) = P(E)
                               then P(F|E) = P(F)

Back to MA375, Fall 2008, Prof. Walther

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Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

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