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Bayesian analysis with a discrete distribution

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Bayesian analysis with a discrete distribution Source: Stattrek.com Bayes Theorem Probability of event A given event B depends not only on the relationship between A ... – PowerPoint PPT presentation

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Title: Bayesian analysis with a discrete distribution


1
Bayesian analysis with a discrete distribution
  • Source Stattrek.com

2
Bayes Theorem
  • Probability of event A given event B depends
    not only on the relationship between A and B but
    on the absolute probability of A not concerning
    B and the absolute probability of B not
    concerning A.
  • Start with --P(AknB)P(Ak).P(BAk)

3
Bayes Theorem
  • If A1, A2, A3An are mutually exclusive events of
    sample space S and B is any event from the same
    sample space and P(B)gt0. then,
  • P(AkB) P(Ak). P(BAk)
  • P(A1). P(BA1)P(A2). P(BA2) P(An).
    P(BAn)

4
Sufficient conditions for Bayes
  • The sample space is partitioned into a set of
    mutually exclusive events A1, A2, . . . , An .
  • Within the sample space, there exists an event B,
    for which P(B) gt 0.
  • The analytical goal is to compute a conditional
    probability of the form P( Ak B ).
  • We know at least one of the two sets of
    probabilities described below.
  • P( Ak n B ) for each Ak
  • P( Ak ) and P( B Ak ) for each Ak

5
Eg. of Discrete Distribution
  • Marie is getting married tomorrow, at an outdoor
    ceremony in the desert. In recent years, it has
    rained only 5 days each year.

6
Eg. of Discrete Distribution
  • The weatherman has predicted rain for tomorrow.
  • When it actually rains, the weatherman correctly
    forecasts rain 90 P( B A1 )of the time.
  • When it doesn't rain, he incorrectly forecasts
    rain 10 P( B A2 ) of the time.
  • What is the probability that it will rain on the
    day of Marie's wedding (P(A1))?
  • A2does not rain on wedding.

7
Values for calculation
  • P( A1 ) 5/365 0.0136985 It rains 5 days out
    of the year.
  • P( A2 ) 360/365 0.9863014 It does not rain
    360 days out of the year.
  • P( B A1 ) 0.9 When it rains, the weatherman
    predicts rain 90 of the time.
  • P( B A2 ) 0.1 When it does not rain, the
    weatherman predicts rain 10 of the time.

8
Applying the Bayes theorem
  • P(AkB) P(Ak). P(BAk)
  • P(A1). P(BA1)P(A2). P(BA2) P(An).
    P(BAn)
  • P(A1B) P(A1). P(BA1)
  • P(A1). P(BA1)P(A2).
    P(BA2)
  • P(A1B) 0.111
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