In my posting prior to this one, I gave an example of two probabilities influencing yet a third. To do that, I assumed a probability for "A" and I assumed a probability for "B", both of which jointly influence "C". But, I gave no evidence that either of these assumptions was "calibrated" by prior experience.

**I just guessed**

**Oh, that's Bayes!**

- If it's a green field -- no experience, no history -- then guess 50/50, 1 chance in 2, a flip of the coin
- Else: use your experience and history to guess other than 1 chance in 2

**According to conditions**

- There is "X" and there is "Y", but "X" in the presence of "Y" may influence outcomes differently.
- In order to get started, one has to make an initial guesses in the form of a hypothesis about not only the probabilistic performance of "X" and "Y", but also about the the influence of "Y" on "X"
- Then the hypothesis is tested by observing outcomes, all according to the parameters one guessed, and
- Finally, follow-up with adjustments until the probabilities better fit the observed variations.

**Always think Bayesian!**

- To get off the dime, make an assumption, and test it against observations
- Adjust, correct, and move on!

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