A stopping time with respect to a sequence of random variables X1, X2, ... is a random variable with the property that for each t, the occurrence or non-occurrence of the event = t depends only on the values of X1, X2, ..., Xt. In some cases, the definition specifies that Pr( < ∞) = 1, or that be almost surely finite, although in other cases this requirement is omitted.
Another, more general definition may be given in terms of a filtration: Let be an ordered index set (often or a compact subset thereof), and let be a filtered probability space, i.e. a probability space equipped with a filtration. Then a random variable is called a stopping time if for all in . Often, to avoid confusion, we call it a -stopping time and explicitly specify the filtration. Speaking concretely, for to be a stopping time, it should be possible to decide whether or not has occurred on the basis of the knowledge of, i.e., event is -measurable.
Stopping times occur in decision theory, in which a stopping rule is characterized as a mechanism for deciding whether to continue or stop a process on the basis of the present position and past events, and which will almost always lead to a decision to stop at some time.
Read more about Stopping Time: Examples, Localization, Types of Stopping Times
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—Brian Moore (b. 1921)
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—Henry David Thoreau (1817–1862)