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Question: Suppose thatandare events in a sample space andp(E)=13,p(F)=12, and p(EF)=25. Findp(FE).

Short Answer

Expert verified

Answer:

The value ofpFE is35 .

Step by step solution

01

Given data

In a sample space,E and Fare the events.

p(E)=13,p(F)=12, andpEF=25

The value of pFEis to be found.

02

Definition and formula to be used

The likelihood of an event or outcome occurring owing to the existence of a preceding event or outcome is defined as conditional probability.

The formula for conditional probability is,

pAB=p(AB)p(B)

Here,A,B are two events,p(AB) is the probability of intersection of the two events, p(B)is the probability of the second event, and pABis the conditional probability.

03

Find the conditional probability

In the formulapEF=p(EF)p(F) , substitute the given values.

25=p(EF)12p(EF)=25×12p(EF)=15

Now, substitute the values in the formula pFE=p(EF)p(E).

pFE=1513pFE=15×31pFE=35

Hence, the value ofpFE is 35.

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Most popular questions from this chapter

Question: Devise a Monte Carlo algorithm that determines whether a permutation of the integers 1 through n has already been sorted (that is, it is in increasing order), or instead, is a random permutation. A step of the algorithm should answer “true” if it determines the list is not sorted and “unknown” otherwise. After k steps, the algorithm decides that the integers are sorted if the answer is “unknown” in each step. Show that as the number of steps increases, the probability that the algorithm produces an incorrect answer is extremely small. [Hint: For each step, test whether certain elements are in the correct order. Make sure these tests are independent.]

Question:To determine which is more likely: rolling a total of 8 when two dice are rolled or rolling a total of 8 when three dice are rolled.

Question:To determine which is more likely: rolling a total of 9 when two dice are rolled or rolling a total of 9 when three dice are rolled?

Question: Prove Theorem \(2\), the extended form of Bayes’ theorem. That is, suppose that \(E\) is an event from a sample space \(S\) and that \({F_1},{F_2},...,{F_n}\) are mutually exclusive events such that \(\bigcup\nolimits_{i = 1}^n {{F_i} = S} \). Assume that \(p\left( E \right) \ne 0\) and \(p\left( {{F_i}} \right) \ne 0\) for \(i = 1,2,...,n\). Show that

\(p\left( {{F_j}\left| E \right.} \right) = \frac{{p\left( {E\left| {{F_j}} \right.} \right)p\left( {{F_j}} \right)}}{{\sum\nolimits_{i = 1}^n {p\left( {E\left| {{F_i}} \right.} \right)p\left( {{F_i}} \right)} }}\)

(Hint: use the fact that \(E = \bigcup\nolimits_{i = 1}^n {\left( {E \cap {F_i}} \right)} \).)

Question 2. To show

If eventsEandFare independent, then events E¯and F¯are also independent.

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