Since Probable Correlation P(A<-->B) = P(AB) + P(A ' B ' ) in Probable Causation/Influence (PI) and E. L. Lehmann's Statistical Quadrant Dependence (Positive or Negative - "Some concepts of dependence," Ann. Math. Stat. 37 (1966) 1137-1153) which can be expressed as DEP(A, B) = P(AB) - P(A)P(B) both measure the Probabilistic/Statistical strength of the relationship between sets/events and between random variables, a key question is: 1) What is the mathematical relationship between P(A<-->B) and DEP(A, B)? If we try to equate them, we get: 2) P(A<-->B) = P(AB) + P(A ' B ' ) = DEP(A, B) = P(AB) - P(A)P(B) ? which yields: 3) P(A ' B ' ) = - P(A)P(B) if (2) is correct. Since probabilities are always nonnegative at least in this Universe, (3) cannot hold, but there are some surprising things about (3). We can easily prove that: 4) P(A ' B ' ) = P{(A U B) ' } = 1 - P(A U B) and substituting from (4) for P(A ' B ' ) into (3) yields: 5) 1 - P(A U B) = - P(A)P(B) if (2) is correct. Now, P(A U B) and P(A)P(B) both increase together, even though the first is "set-additive" and the second is real-multiplicative. So (5) is plausible under at least a wide range of conditions except for the constant 1, or at least a modification of (5): 6) 1 - P(A U B) = -kP(A)P(B), k a positive real number. One "natural" suggestion is to change to complex variables so that a 1 appears on the right hand side of (6) from -i^2 = 1 with a modification in (2). Another possibility is to change the definition of Statistical Dependence by simply adding 1 to it and replacing -P(A)P(B) by -kP(A)P (B) for k real positive number in its definition above (1). That is: 7) Modified DEP(A, B) = 1 + P(AB) - kP(A)P(B), k > 0 Then the same procedure as above with DEP(A, B) replaced by Modified DEP(A, B) yields: 8) P(A U B) = kP(A)P(B), k > 0. Although this may not always hold for all sets/events, it is plausible under a wide variety of conditions. Osher Doctorow Other posts:
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