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Question: \[ {\text{There are two coins, one unbiased with probability }}\dfrac{1}{2}{\text{\;}}{\text{\;o...

There are two coins, one unbiased with probability 12    of getting heads and the other one is biased with probability  34  of getting heads. A coin is selected at random and tossed.  It shows heads up. Then, the probability that the unbiased coin was selected is  (A)   23 (B)  35 (C)   12 (D) 25  {\text{There are two coins, one unbiased with probability }}\dfrac{1}{2}{\text{\;}}{\text{\;of getting heads and the other one}} \\\ {\text{is biased with probability\;}}\dfrac{3}{4}{\text{\;of getting heads}}{\text{. A coin is selected at random and tossed}}{\text{. }} \\\ {\text{It shows heads up}}{\text{. Then, the probability that the unbiased coin was selected is }} \\\ {\text{(A)\; }}\dfrac{2}{3}{\text{ (}}{\text{B)\;}}\dfrac{3}{5} \\\ ({\text{C)\; }}\dfrac{1}{2}{\text{ (}}{\text{D) }}\dfrac{2}{5} \\\
Explanation

Solution

Let EEvent of head showing up Let E1Event of biased coin chosen Let E2Event of unbiased coin chosen Now as we know that P(x) is the probability of occurrence of x. As the probability of occurrence of E1 and E2 is same so, Now, P(E2)=12 and P(E1)=12 So, by conditional probability we know that P(xy) is the probability of occurrence of x if  it is known that y has already occured So as given in question, P(EE2)=12 and P(EE1)=34  So, now we have to find the probability that coin selected is unbiased if it is known  that the coin we get is head showing up. And this is equal to P(E2E) So, now by using Baye’s theorem  P(E2E)=P(E2)P(EE2)P(E2)P(EE2)+P(E1)P(EE1) (1) Putting all values on equation 1 we get,  P(E2E)=12121212+1234 = 1414+38 = 1458 = 25 So, probability that coin selected is unbiased if it is known that the coin we get is  head showing up = P(E2E)=25 Hence correct option is D NOTE: - Whenever you came up with this type of problem then you have to apply Baye’s theorem. So, you should always remember Baye’s theorem.  {\text{Let E}} \to {\text{Event of head showing up}} \\\ {\text{Let }}{{\text{E}}_1} \to {\text{Event of biased coin chosen}} \\\ {\text{Let }}{{\text{E}}_2} \to {\text{Event of unbiased coin chosen}} \\\ {\text{Now as we know that }}P(x){\text{ is the probability of occurrence of x}}{\text{.}} \\\ {\text{As the probability of occurrence of }}{E_1}{\text{ and }}{E_2}{\text{ is same so,}} \\\ \Rightarrow {\text{Now, }}P({E_2}) = \dfrac{1}{2}{\text{ and }}P({E_1}) = \dfrac{1}{2} \\\ {\text{So, by conditional probability we know that }}P\left( {\dfrac{x}{y}} \right){\text{ is the probability of occurrence of x if }} \\\ {\text{it is known that y has already occured}} \\\ {\text{So as given in question,}} \\\ \Rightarrow P\left( {\dfrac{E}{{{E_2}}}} \right) = \dfrac{1}{2}{\text{ and }}P\left( {\dfrac{E}{{{E_1}}}} \right) = \dfrac{3}{4}{\text{ }} \\\ {\text{So, now we have to find the probability that coin selected is unbiased if it is known }} \\\ {\text{that the coin we get is head showing up}}{\text{.}} \\\ {\text{And this is equal to }}P\left( {\dfrac{{{E_2}}}{E}} \right) \\\ {\text{So, now by using Baye's theorem}} \\\ \Rightarrow {\text{ }}P\left( {\dfrac{{{E_2}}}{E}} \right) = \dfrac{{P({E_2})*P\left( {\dfrac{E}{{{E_2}}}} \right)}}{{P({E_2})*P\left( {\dfrac{E}{{{E_2}}}} \right) + P({E_1})*P\left( {\dfrac{E}{{{E_1}}}} \right)}}{\text{ }}\left( 1 \right) \\\ {\text{Putting all values on equation 1 we get,}} \\\ \Rightarrow {\text{ }}P\left( {\dfrac{{{E_2}}}{E}} \right) = \dfrac{{\dfrac{1}{2}*\dfrac{1}{2}}}{{\dfrac{1}{2}*\dfrac{1}{2} + \dfrac{1}{2}*\dfrac{3}{4}}}{\text{ = }}\dfrac{{\dfrac{1}{4}}}{{\dfrac{1}{4} + \dfrac{3}{8}}}{\text{ = }}\dfrac{{\dfrac{1}{4}}}{{\dfrac{5}{8}}}{\text{ }} = {\text{ }}\dfrac{2}{5} \\\ {\text{So, probability that coin selected is unbiased if it is known that the coin we get is }} \\\ {\text{head showing up = }}P\left( {\dfrac{{{E_2}}}{E}} \right) = \dfrac{2}{5} \\\ {\text{Hence correct option is D}} \\\ {\text{NOTE: - Whenever you came up with this type of problem then you have to apply Baye's}} \\\ {\text{theorem}}{\text{. So, you should always remember Baye's theorem}}{\text{.}} \\\