**WHEN
MODELING SYSTEMS THAT EXHIBIT SOME FORM OF RANDOMNESS, THE CHALLENGE IN
THE MODELING** process is to find a way to handle
the resulting uncertainty. We don’t know for sure what the system will
do—there is a range of outcomes, each of which is more or less likely,
according to some probability distribution. Occasionally, it is possible
to work out the exact probabilities for all possible events; however,
this quickly becomes very difficult, if not impossible, as we go from
simple (and possibly idealized systems) to real applications. We need to
find ways to simplify life!

In this chapter, I want to take a look at some of the “standard” probability models that occur frequently in practical problems. I shall also describe some of their properties that make it possible to reason about them without having to perform explicit calculations for all possible outcomes. We will see that we can reduce the behavior of many random systems to their “typical” outcome and a narrow range around that.

This is true for many situations but not for all! Systems
characterized by power-law distribution functions can
*not* be summarized by a narrow regime around a
single value, and you will obtain highly misleading (if not outright
wrong) results if you try to handle such scenarios with standard
methods. It is therefore important to recognize this kind of behavior
and to choose appropriate techniques.

Bernoulli trials ...

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