Examples with Bayes Theorem (quantitative symptoms)

`prevalence_MI = 0.2` `ck_without_MI = moments([50,34])` `ck_within_MI = moments([200,140])`	Prevalence, sensitivity and specificy are used in the Bayes Theorem to calculate an aposterior probability p(D\|X) where `p(D) * L(X,D)` `p(D\|X) = ------------------------` `p(D) * L(X,D) + 1 - p(D)` `with` `p(X\|D)` `L(X,D) = ---------` `p(X\|notD)` `where e.g.` `p(D) = 0.2 (prevalence)` `p(X\|D) = moments([50,34])` `(semiknown CK within` `myocardial infarction)` `p(X\|notD) = moments([200,140])` `(semiknown CK without` `myocardial infarction)`
If CK is 120 then the predictive value (aposteriori probability) becomes	.
`aposteriori = 0.2605`	The FunLog++ interpreter calculates the aposteriory probability for a CK test result of 100 according to the Bayes theorem.

FunLog++ message	Explanation
`X = 90,` `PXD = moments([190,256]),` `PXnotD = moments([50,36]),` `PD = 0.2,` `PDX = bayes(PD,PXD,PXnotD,X),` `display( "P(D\|X=" # X # ") = " # PDX )`	The message on the left hand side calculated the aposteriori probability P(D\|X) with respect to sensitivity PXD, unspecificy PXnotD and symptom X. The FunLog++ interpreter evaluates this term and generates an appropriate probability. Click on the message link to see the result of this calculation which is displayed. `P(D\|X=100) = 0.0.5803`

Last modification: B. Pohl 22.August 2000 / 07.November 2000