By Finn V. Jensen (auth.)

ISBN-10: 1475735022

ISBN-13: 9781475735024

ISBN-10: 1475735049

ISBN-13: 9781475735048

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**Additional resources for Bayesian Networks and Decision Graphs**

**Example text**

Yes). 01. The numbers provided by the retailer are not sufficient for the user of the test. ). An estimate of the prior probability would in this case be the daily frequency A of infected milk for each cow at the particular farm. To estimate A may be a bit tricky because the farmer may have no experience with actually testing the milk from each specific cow with a perfect test. Assume that this particular farm has 50 cows, and the milk from all cows is poured into a container and transported to the dairy, which tests the milk with a very precise test.

The hypothesis events detected are then grouped into sets of mutually exclusive events to form hypothesis variables. The next thing to have in mind is that in order to come up with a certainty estimate, we should provide some information channels, and the task is to identify the types of achievable information that may reveal something about the hypothesis variables. These types of information are grouped into information variables, and a typical piece of information is a statement that a certain variable is in a particular state, but softer statements are also allowed.

7, the results of approximate combinatorial calculations are given. 7. 0408 0 FC) for the nonobvious parent configurations. The probabilities for the remaining parent configurations may be whatever is convenient, so put, for example, P( OH1 I 3 v, 1) = (1,0, ... ,0). 7 can be calculated.

### Bayesian Networks and Decision Graphs by Finn V. Jensen (auth.)

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