From Introduction to Probability.
Discrete Uniform over [a,b]
When X can take any value from a to b(both inclusive) with equal probability.
= if k = a, a+1, a+2, ..., b-1, b
= 0 otherwise
E[X] =
var(X) =
Bernoulli with Parameter p
It describes the success or failure in a single trial. For example, we can say that X = 1 if a toss results in HEAD or else X = 0 if the toss results in TAIL and probability of HEAD is p.
= p if k = 1
= 1-p if k = 0
E[X] = p
var(X) = p(1-p)
Binomial with Parameters p and n
It describes the number of successes in n independent bernoulli trials. For example, total number of HEADs obtained in n tosses.
= , if k = 0,1,2,...n
E[X] = np
var(X) = np(1-p)
Also, we can use bernoulli random variables to model binomial random variable as follow.
Let are n independent Bernoulli Random variables with parameter p. Then
Binomial Random Variable, X =
Geometric with Parameter p
It describes the number of trials until the first success, in a sequence of independent Bernoulli trials. For example, the number of tosses required to get first HEAD.
= , if k = 1,2,....
E[X]] =
var(X) =
Poisson with Parameter
It approximates the binomial PMF when n is large and p is small and = np
= , k = 0,1,2...
E[X] = = var(X)
Sunday, September 19, 2010
Subscribe to:
Post Comments (Atom)
Its easy to learn random and discrete variable theory but their numerical poses problem.Displacement Formula
ReplyDelete