Thinking about recursion and self-organisation has led me down the scary road of Bayes Filters and Theorem. Wikipedia promises: A Bayes filter is an algorithm used in computer science for calculating the probabilities of multiple beliefs to allow a robot to infer its position and orientation. Which sounds pretty sexy. I was panicy over Bayes Theorem but it turns out, its pretty simple. I can thank IBM for a simple explanation. I had made a start on the understanding when I talked about Bob the agent that never took off, I thought about frequency counts P(A | B) instead of probabilities. Recasting this foruma as `P(A | B) = P(A & B) / P(B)` That is the probability of observing A given that B has happened equals the probability of a and b happeneing divided by the probability of B .We now have a definition of conditional probability. It’s only a few steps from here to Bayes theorem. `1) Rewrite the equation:` `P(A & B) = P(A | B) P(B)` `2) change A to the conditiong variable:` `P(A & B) = P(B | A) P(A)` `3) You can write:` `P(A | B) P(B) = P(B | A) P(A)` `4) You can simplify` `P(A | B) = P(B | A) P(A) / P(B)` Tada. Baynes therom is: `p(hypothesis|data) = p(data|hypothesis) x p(hypothesis) / p(data).` Suddenly the whole world makes sense. A code example to follow. Now on to recursive bayesian estimation… See also http://phpir.com/bayesian-opinion-mining http://www.ibm.com/developerworks/web/library/wa-bayes1/ |

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