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I found an example in wikipedia stating:

Suppose that there are two events which could cause grass to be wet: either the sprinkler is on or it's raining. Also, suppose that the rain has a direct effect on the use of the sprinkler (namely that when it rains, the sprinkler is usually not turned on). Then the situation can be modeled with a Bayesian network (shown to the right). All three variables have two possible values, T (for true) and F (for false).

I'm having problems in understanding whether the values in the table(the one with four entries) are assumed or calculated from other small tables example

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All of the tables appear to be assumed. You can get to this conclusion pretty quickly by observing that the prior tables say nothing about wet grass, so you need something to tie them to wet grass.

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The probabilities in the tables are the expected probabilities relating the inputs and outputs. They are typically developed based on observations / test data, subject matter expertise opinions, or WAGs :-).

I have some small (very small) association with using this technique as a trade study tool in concept development.

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