Let E1=5 or 6 appears on a die,E2=1,2,3 or 4 appears on a die and A=A head appears on the coin.
Now P(E1)=\(\frac{2}{6}\)=\(\frac{1}{3}\),P(E2)=\(\frac{4}{6}\)=\(\frac{2}{3}\)
Now P(A|E1)Probability of getting a head on tossing a coin three times,
When E1 has already occurs=P(HTT)or P(THT)or P(TTH)
\(=\frac{1}{2}\times\frac{1}{2}\times\frac{1}{2}+\frac{1}{2}\times\frac{1}{2}\times\frac{1}{2}+\frac{1}{2}\times\frac{1}{2}\times\frac{1}{2}=\frac{3}{8}\)
P(A|E2)=Probability of getting a head on tossing a coin once,
When E2 has already occured=\(\frac{1}{2}\)
∴P(there is exactly one head given that 1,2,3 or 4 appears on a die)
\(P(E_2|A)=\frac{P(E_2)P(A|E_2)}{P(E_1)P(A|E_1)+P(E_2)P(A|E_2)}\)
\(=\frac{\frac{2}{3}\times\frac{1}{2}}{\frac{1}{3}\times\frac{3}{8}+\frac{2}{3}\times\frac{1}{2}}\)\(=\frac{\frac{1}{3}}{\frac{1}{8}+\frac{1}{3}}\)\(=\frac{24}{3}\times11\)=\(\frac{24}{33}\)=\(\frac{8}{11}\)
What is the Planning Process?
Bayes’ Theorem is a part of the conditional probability that helps in finding the probability of an event, based on previous knowledge of conditions that might be related to that event.
Mathematically, Bayes’ Theorem is stated as:-
\(P(A|B)=\frac{P(B|A)P(A)}{P(B)}\)
where,
This formula confines well as long as there are only two events. However, Bayes’ Theorem is not confined to two events. Hence, for more events.