Lecture 16 - Binomial &amp; Bernoulli

# Lecture 16 - Binomial &amp; Bernoulli - 1 BERNOULLI AND...

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Unformatted text preview: 1 BERNOULLI AND BINOMIAL DISTRIBUTIONS Lecture 16 ORIE3500/5500 Summer2011 Li 1 Bernoulli and Binomial distributions A Bernoulli is the simplest random variable of all(after the constant). It takes the values 1 and 0 with probabilities p and 1- p respectively. One views this as whether an experiment is a success or a failure. It is 1 in case of success and 0 in case of a failure. So if X is a Bernoulli( p ) random variable then the pmf of X is p X (0) = 1- p,p X (1) = p. We also saw earlier that E ( X ) = p , E ( X 2 ) = p and var ( X ) = p (1- p ). The cdf of X will be F X ( x ) = x &amp;lt; , 1- p ≤ x &amp;lt; 1 , 1 1 ≤ x. The moment generating function of X is φ X ( t ) = (1- p ) + pe t . Now suppose there are n independent experiments and each can be a success with probability p and a failure with probability 1- p . We might be interested in the probability distribution. Let X be the total number of successful experiments. If Y i is 1 if the i th experiment is a success, then X = Y 1 + ··· + Y n ., where Y i ’s are independent Bernoulli( p ) random variables. This X is said to be a binomial random variable with parameters n and p , bin ( n,p ). A bin ( n,p ) random variable X can take the values 0...
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• Spring '11
• SONG
• Probability theory, Binomial distribution, Cumulative distribution function, Discrete probability distribution, 2 K

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Lecture 16 - Binomial &amp; Bernoulli - 1 BERNOULLI AND...

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