Law of total probability - you could have invented this
A knight wants to kill (event ) a dragon. There are two ways to do this: by fighting (event
) the dragon or by outwitting (
) it. The choice of the way (
or
) is random, and in each case the outcome (
or not
) is also random. For the probability of killing there is a simple, intuitive formula:
.
Its derivation is straightforward from the definition of conditional probability: since and
cover the whole sample space and are disjoint, we have by additivity of probability
.
This is easy to generalize to the case of many conditioning events. Suppose are mutually exclusive (that is, disjoint) and collectively exhaustive (that is, cover the whole sample space). Then for any event
one has
.
This equation is call the law of total probability.
Application to a sum of continuous and discrete random variables
Let be independent random variables. Suppose that
is continuous, with a distribution function
, and suppose
is discrete, with values
. Then for the distribution function of the sum
we have
(by independence conditioning on can be omitted)
.
Compare this to the much more complex derivation in case of two continuous variables.
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