Conditional Probability: Bayes' Theorem

Bayes’ theorem describes the probability of occurrence of an event related to any condition. For example: if we have to calculate the probability of taking a blue ball from the second bag out of three different bags of balls, where each bag contains three different color balls viz. red, blue, black. Such case where probability of occurrence of an event is calculated depending on other conditions is known as conditional probability.

Conditional Probability

Derivation of Bayes Theorem:

Statement:Let \(E_1, E_2,…,E_n\)} be a set of events associated with a sample space \(S\), where all the events \(E_1, E_2,…, E_n\) have nonzero probability of occurrence and they form a partition of \(S\). Let \(A\) be any event associated with \(S\), then according to Bayes theorem,

\(P(E_i│A)~=~\frac{P(E_i)P(E_i│A)}{\sum\limits_{k=0}^{n}P(E_k)P(A| E_k)}\)

Proof:According to conditional probability formula,

\(P(E_i│A)~=~\frac{P(E_i ∩ A)}{P(A)}\) ⋯⋯⋯⋯⋯⋯⋯⋯(1)

Using multiplication rule of probability,
\(P(E_i ∩ A)~= ~P(E_i)P(E_i│A)\)⋯⋯⋯⋯⋯⋯⋯⋯(2)

Using total probability theorem,
\(P(A)~=~\sum\limits_{k=0}^{n}~P(E_k)P(A| E_k)\)⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯(3)

Putting the values from equations (2) and (3) in equation 1, we get

\(P(E_i│A)~=~\frac{P(E_i)P(E_i│A)}{\sum\limits_{k=0}^n~P(E_k)P(A| E_k)}\)

Examples:

Some illustrations will improve the understanding of the concept.

Example 1:Bag I contains 4 white and 6 black balls while another Bag II contains 4 white and 3 black balls. One ball is drawn at random from one of the bags and it is found to be black. Find the probability that it was drawn from Bag I.

Solution:Let \(E_1\) be the event of choosing the bag I, \(E_2\) the event of choosing the bag II and A be the event of drawing a black ball.

Then,\(P(E_1)~ = ~P(E_2)~ =~\frac{1}{2}\)

Also,\(P(A|E_1) ~= ~P\)(drawing a black ball from Bag I) = \(\frac{6}{10}~ = ~\frac{3}{5}\)

\(P(A|E_2) ~=~ P\)(drawing a black ball from Bag II) = \(\frac{3}{7}\)

By using Bayes’ theorem, the probability of drawing a black ball from bag I out of two bags,

\(P(E_1 |A)~ =~\frac{P(E_1)P(A|E_1)}{P(E_1 )P(A│E_1 )+ P(E_2)P(A|E_2)}\)

=\(\large\frac{\frac{1}{2}~\times~\frac{3}{5}}{\frac{1}{2}~\times~\frac{3}{7}~+~\frac{1}{2}~ ×~\frac{3}{5}}\) = \(\frac{7}{12}\)

Example 2:A man is known to speak truth 2 out of 3 times. He throws a die andreports that number obtained is a four. Find the probability that the number obtained is actually a four.

Solution:Let \(A\) be the event that the man reports that number four is obtained.

Let \(E_1\) be the event that four is obtained and \(E_2\) be its complementary event.

Then, \(P(E_1)\) = Probability that four occurs = \(\frac{1}{6}\)

\(P(E_2)\) = Probability that four does not occurs = \(1 ~–~ P(E_1) ~=~ 1~-\frac{1}{6}~ =~\frac{5}{6}\)

Also, \(P(A|E_1)\) = Probability that man reports four and it is actually a four = \(\frac{2}{3}\)

\(P(A|E_2)\) = Probability that man reports four and it is not a four = \(\frac{1}{3}\)

By using Bayes’ theorem, probability that number obtained is actually a four,

\(P(E_1 |A)~ \) \(= \large \frac{P(E_1)P(A|E_1)}{P(E_1 )P(A│E_1 )~+~ P(E_2)P(A|E_2)}~
=~\frac{\frac{1}{6} ~ ×~ \frac{2}{3}}{\frac{1}{6} ~×~ \frac{2}{3}~ +~ \frac{5}{6}~ ×~\frac{1}{3}}\)
= \(\frac{2}{7}\)

Students, are you struggling to find a solution to a specific question from Bayes theorem? We will make it easy for you. For detailed discussion on the concept of Bayes’ theorem, download Byju’s-the learning app

Practise This Question

There are 5 students who play guitar in class XI and 8 students who play guitar in class XII. Each class has 50 students. The probability of choosing class XI is 25 and the probability of choosing class XII is 35. A student is chosen and is found to be playing guitar. If the probability that the chosen student is from class XI is p, then the value of 34p is equal to

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