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MTH 263Probability and Random Variables                 Lecture 5, Chapter 3                     Dr. Sobia Baig         El...
Contents• Binomial Probability Law• Examples• Multinomial Law          Probability and Random Variables, Lecture 5, by Dr....
The Binomial Probability Law• A Bernoulli trial involves performing an experiment once  and noting whether a particular ev...
Example-Bernoulli Law• Suppose that a coin is tossed three times.• If we assume that the tosses are independent and the pr...
Example-Bernoulli Law …• Let k be the number of heads in three  trials, then          Probability and Random Variables, Le...
Binomial Probability Theorem• Let k be the number of successes in n independent Bernoulli  trials, then the probabilities ...
Probability and Random Variables, Lecture 5, by Dr. Sobia Baig   7
• which are in agreement with our previous  results          Probability and Random Variables, Lecture 5, by Dr. Sobia Bai...
Example• Let k be the number of active (nonsilent) speakers in a  group of eight noninteracting (i.e., independent)  speak...
Probability and Random Variables, Lecture 5, by Dr. Sobia Baig   10
Error Correction Coding• A communication system transmits binary information over a channel that  introduces random bit er...
Probability and Random Variables, Lecture 5, by Dr. Sobia Baig   12
The Multinomial Probability Law      Probability and Random Variables, Lecture 5, by Dr. Sobia Baig   13
Probability and Random Variables, Lecture 5, by Dr. Sobia Baig   14
Example• Suppose we pick 10 telephone numbers at random  from a telephone book and note the last digit in each  of the num...
SummaryProbability and Random Variables, Lecture 5, by Dr. Sobia Baig   16
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  1. 1. MTH 263Probability and Random Variables Lecture 5, Chapter 3 Dr. Sobia Baig Electrical Engineering Department COMSATS Institute of Information Technology, Lahore
  2. 2. Contents• Binomial Probability Law• Examples• Multinomial Law Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 2
  3. 3. The Binomial Probability Law• A Bernoulli trial involves performing an experiment once and noting whether a particular event A occurs.• The outcome of the Bernoulli trial is said to be a “success” if A occurs and a “failure” otherwise.• we are interested in finding the probability of k successes in n independent repetitions of a Bernoulli trial.• We can view the outcome of a single Bernoulli trial as the outcome of a toss of a coin for which the probability of heads (success) is• The probability of k successes in n Bernoulli trials is then equal to the probability of k heads in n tosses of the coin. Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 3
  4. 4. Example-Bernoulli Law• Suppose that a coin is tossed three times.• If we assume that the tosses are independent and the probability of heads is p, then the probability for the sequences of heads and tails is• where we used the fact that the tosses are independent. Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 4
  5. 5. Example-Bernoulli Law …• Let k be the number of heads in three trials, then Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 5
  6. 6. Binomial Probability Theorem• Let k be the number of successes in n independent Bernoulli trials, then the probabilities of k are given by the binomial probability law: Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 6
  7. 7. Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 7
  8. 8. • which are in agreement with our previous results Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 8
  9. 9. Example• Let k be the number of active (nonsilent) speakers in a group of eight noninteracting (i.e., independent) speakers.• Suppose that a speaker is active with probability 1/3. Find the probability that the number of active speakers is greater than six.• For let denote the event “ith speaker is active.”• The number of active speakers is then the number of successes in eight Bernoulli trials with p=1/3• Thus the probability that more than six speakers are active is: Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 9
  10. 10. Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 10
  11. 11. Error Correction Coding• A communication system transmits binary information over a channel that introduces random bit errors with probability• The transmitter transmits each information bit three times, and a decoder takes a majority vote of the received bits to decide on what the transmitted bit was.• Find the probability that the receiver will make an incorrect decision.• The receiver can correct a single error, but it will make the wrong decision if the channel introduces two or more errors.• If we view each transmission as a Bernoulli trial in which a “success” corresponds to the introduction of an error, then the probability of two or more errors in three Bernoulli trials is Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 11
  12. 12. Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 12
  13. 13. The Multinomial Probability Law Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 13
  14. 14. Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 14
  15. 15. Example• Suppose we pick 10 telephone numbers at random from a telephone book and note the last digit in each of the numbers.• What is the probability that we obtain each of the integers from 0 to 9 only once?• The probabilities for the number of occurrences of the integers is given by the multinomial probabilities with parameters and if we assume that the 10 integers in the range 0 to 9 are equiprobable.• The probability of obtaining each integer once in 10 draws is then Probability and Random Variables, Lecture 5, by Dr. Sobia Baig 15
  16. 16. SummaryProbability and Random Variables, Lecture 5, by Dr. Sobia Baig 16
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