Ex. For a Bernoulli random variable, introducing a parameter α gives us the Bernoulli distribution: p(x;α)={α1−αx=1x=0 To say that a random variable x follows this distribution, we write x→Bernoulli(α). For example, if x is the number of heads that occur from a coin toss, then x→Bernoulli(0.5).
Ex. Geometric distribution with parameter p:
Suppose we’re testing the battery life of batteries. Let p=P({S}). x is the number of tests we must do before finding a battery with acceptable battery life (an S). Then: p(1)p(2)p(3)p(k)=P(x=1)=p=P(x=2)=(1−p)p=P(x=3)=(1−p)2p=P(x=k)=(1−p)k−1p In general, p(k;p)={(1−p)k−1p0k=1,2,3...otherwise
We say that this x follows a geometric distribution with parameter p: x→Geo(p).
Definition. The cumulative distribution function (cdf) F(x) of a discrete random variable x with probability mass function p(x) is defined for every x by F(x)=P(X≤x)=y: y≤x∑p(y).
Ex. Converting a cdf to a geometric distribution with p: In general, F(x)=y: y≤x∑p(y;p)=y=1∑⌊x⌋p(y;p)=y=1∑⌊x⌋(1−p)y−1p.
Now we can apply the formula for the sum of a geometric series, which is: y=0∑kay=1−a1−ak+1 if 0<a<1.
F(x)=py=1∑⌊x⌋(1−p)y−1=py=0∑⌊x⌋−1(1−p)y=p⋅1−(1−p)1−(1−p)⌊x⌋=1−(1−p)⌊x⌋