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Discrete Probability Distributions

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E.g. A man practices shooting. If he hits the target with probability 0.7, what is the probability that he is ... 10 times in order to hit the targets 7 times? ... – PowerPoint PPT presentation

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Title: Discrete Probability Distributions


1
Tutorial 1
  • Discrete Probability Distributions

2
Bernoulli Distribution
  • The outcome of some kind of trials can be either
    a success or a failure. (e.g. tossing a coin)
  • Denote X 1 if the outcome is a success and X
    0 if the outcome is a failure.
  • If P(X 1) p, then P(X 0) 1 - p.
  • We said X is a Bernoulli random variable.

3
Bernoulli Distribution
  • Mean of X EX (1-p)0 p1
  • p
  • Variance of X Var(X) (1-p)(0-EX)2
  • p(1-EX)2
  • (1-p)p

4
Binomial Distribution
  • If we perform Bernoulli trials n times, then the
    no. of successes X is binomial distributed.
  • It is usually denoted by
  • where n total no. of trials
  • p probability of success

5
Binomial Distribution
  • Each trial in binomial distribution is
    independent. (i.e. outcome of each trial will not
    affect others)
  • n is a fixed no.
  • When n 1, the binomial distribution is reduced
    to a Bernoulli distribution.
  • EX np, Var(X) np(1-p)

6
Binomial Distribution
  • E.g. What is the probability that we get 4 heads
    after tossing a coin 10 times?

7
Negative Binomial Distribution
  • Now, suppose the no. of trials is not fixed.
  • We perform Bernoulli trials repeatedly until a
    given no. of successes r are observed. Then we
    stop the trials.
  • We said the no. of trials required X is a
    negative binomial random variable

8
Negative Binomial Distribution
  • There are (r-1) successes in the first (n-1)
    trials.
  • The n-th (last) trial must be a success.
  • EX r/p, Var(X) r(1-p)/p2

9
Negative Binomial Distribution
  • E.g. A man practices shooting. If he hits the
    target with probability 0.7, what is the
    probability that he is required to shoot 10 times
    in order to hit the targets 7 times?

10
Geometric Distribution
  • It is a particular case of -ve binomial distr.
  • Suppose we perform Bernoulli trials until a
    success occurs (i.e. r1).
  • If we let Y equal the no. of trials required
  • Then Y is a geometric random variable.

11
Geometric Distribution
  • Geometric distribution is memoryless
  • Why?

12
Geometric Distribution
  • How many trials should be made on average in
    order to get a head when tossing a coin?
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