Conditional
Probability: Let A and B be two events in a probability space$(\Omega, \tilde{A}, P[.])$. The conditional probability of event A given event B, denoted
by P[A/B] is defined by
$$P[A/B]=\frac{P[AB]}{P[B]}, if ~P[B] >0$$
Similarly $P[B/A]=\frac{P[AB]}{P[A]}, if ~P[A] >0$ is the conditional probaility of event B given event A.
From the above relation, We see that $P[AB]=P[A/B]P[B]= P[B/A]P[A],$
Independent Events: Two events A and B defined on a probability space $(\Omega, \tilde{A}, P[.])$ are said to be independent if $P[AB]=P[A\cap B]=P[A] P[B]$.
Similarly: Any $n$ events $A_1, A_2,...., A_n$ defined on a probability space $(\Omega, \tilde{A}, P[.])$ are said to be independent if and only if the following conditons are satisfied:
$P[A_i A_j] = P[A_i]P[A_j], ~ for ~ i\neq j$
$P[A_i A_j A_k] = P[A_i] P[A_j]P[A_k]~ for i\neq j, i\neq k, j\neq k$
....
....
$P[A_1 A_2 .... A_n]=P[A_1]P[A_2]P[A_3]....P[A_n]$.
Theorem: Show that the following conditions are
equivalent:
- $P[AB]=P[A]P[B]$
- $[A/B]= P[A], ~if ~P[B]>0$.
- $P[B/A]= P[B], ~if~ P[A] >0 $
Theorem: If A and B are independent events, then
- A and $\bar{B}$ are independent.
- $\bar{A}$ and B are independent and
- $\bar{A}$ and $\bar{B}$ are independent.
Solution: Since A and B are independent, therefore all $A, B, \bar{A},$ and $\bar{B}$ are indepedent?
Consequently, $P[A\bar{B}]=P[A]P[\bar{B}]=\frac{1}{2}(1-\frac{1}{2})=\frac{1}{4}$,
Now $P[A\bar{B}\bar{A}B] = P[A\bar{B}]+P[\bar{A}B]-P[A\bar{B}\bar{A}B] = \frac{1}{4}+\frac{1}{4}-P[\phi]=\frac{1}{2}$ as $A\bar{A}=\phi$ and $\bar{B}B=\phi$.
Example
2. Five persons of the people have high
blood pressure. Of the people with high blood pressure, $75\%$ drink alcohol;
whereas, only $50\%$ of the people without high blood pressure drink alcohol. What
percent of the drinkers have high blood pressure?
Solution: Let A denote the event that
people have high blood pressure and B denote the people who
drink alcohol.
We have $P[A]=0.05, P[B/A]=0.75, P[B/\bar{A}]=0.50$
We have to find $P[A/B]=\frac{P[A\cap B]}{P[B]}$ ......(1)
We know $B = AB \cup \bar{A}B$ and $AB \cap \bar{A}B =\phi$
$\Rightarrow P[B] = P[AB] + P[\bar{A}B]= P[A\cap B] + P[\bar{A}\cap B]$....... (2)
Now $P[B/A]=0.75 \Rightarrow \frac{P[B\cap A]}{P[A]}=0.75 \Rightarrow P[A\cap B]= 0.75 \times 0,05 = 0.0375$ .........(3)
Also $P[B/\bar{A}]=0.50 \Rightarrow \frac{P[B\cap \bar{A}]}{P[\bar{A}]}=0.50 \Rightarrow P[B\cap \bar{A}]=\frac{1}{2}(1-P[A])$
$\Rightarrow P[B\cap \bar{A}] = P[B]- P[A\cap B] = \frac{1}{2}(1-0.05)=0.475$ by (2)
Therefore, $P[B]=0.0375 +0.475 = 0.5125$ by (3).
Hence, by (1), $P[A/B]=\frac{P[A\cap B}{P[B]}=\frac{0.0375}{0.5125}=\frac{3}{41}=0.073$
Hence the required percentage is $73\%$.
Exercises:
- If $P[A] = P[B] = P[B/A]=0.5$ are A and B are independent?
- If $P[A]=a, P[B]=b,$ then show that $P[A/B] \geq (a+b-1)/b$.
- Suppose A and B are events for which $P[A]=p_1$, $P[B]=p_2$, and $P[A \cap B]=p_3.$ Evaluate
- $P[\bar{A}\cap B]$
- $P[\bar{A}\cup B]$
- $P[A\cap \bar{B}]$
- $P[\bar{A}\cap \bar{B}]$
- $P[\overline{A \cap B}]$
- $P[\overline{A \cup B}]$
- $P[\bar{A}\cup \bar{B}]$
- $P[A/B]$
- $P[B/\bar{A}]$
- $P[\bar{A}/\bar{B}$
- $P[\bar{A}\cap (A\cup B)]$
- $P[A\cup (\bar{A}\cap B)]$
- Suppose an urn contains $M$ balls of which $k$ are black and $M-K$ are white. A sample of size $n$ is drawn with replacement. Find the probability that the j-th ball drawn is black that the sample contains $K$ black balls.
Theorem of Total Probability:
Let $B_1, B_2,...., B_n$ be a collection of mutually disjoint events in the probability space $(\Omega, \tilde{A}, P[.])$ such that $\Omega = \cup_{j=1}^n B_j$ and $P[B_j]>0, j=1, 2,...,n.$
Then $P[A]= \sum_{j=1}^n P[A/B_j]P[B_j]$ for each $A \in \tilde{A}$
Proof: We have $A = A \cap \Omega = A \cap (\cup_{j=1}^n AB_j)=\sum_{j=1}{n} P[AB_j]$.... (1)
By definition, $P[AB_j]=P[A/B_j]P[B_j]$ ......(2)
Corollary: If $A, B \in \tilde{A};$ then $P[A]=P[A/B]P[B]+P[A/\bar{B}]P[\bar{B}], P[B]>0$
Proof : We have $\Omega = B \cup \bar{B}$, where $B$ and $\bar{B}$ are mutually disjoint.
Hence by the above theorem, $P[A]=P[A/B]P[B]+P[A/\bar{B}]P[\bar{B}], P[B]>0$
Bay's Theorem :
Let $B_1, B_2,....., B_n$ be a collection of mutually disjoint events in the probability space $(\Omega, \tilde{A}, P[.])$ such that $\Omega = \cup_{j=1}^n B_j$ and $P[B_j]>0, j=1, 2,....., n.$
Then for each $A \in \tilde{A}$ satisfying $P[A]>0$, we have
$P[B_k/A] = \frac{P[A/B_k]P[B_k] }{\sum_{j=1}^n P[A/B_j]P[B_j]}$, this is know as Bay's formulla.
Proof: By the definition of conditional probability, we have
$P[B_k/A]=\frac{P[B_kA}{P[A]}$ and $P[A/B_k]=\frac{P[AB_k]}{P[B_k]}$ with $P[A]>0$ and $P[B_k]>0$ ......(1)
Using these two, we obtain $P[B_k/A]=\frac{P[A/B_k]P[B_k]}{P[A]}$ ......(2)
By a theorem of total probability, we have
$P[B_k/A]=\frac{P[A/B_k]P[B_k]}{\sum_{j=1}^n P[A/B_j]P[B_j]}$
Hence proved.
Corollary: If $A, B \in \tilde{A}$ then $P[B/A]=\frac{P[A/B_k]P[B_k]}{P[A/B]P[B]+P[A/\bar{B}]P[\bar{B}]}, P[B]> 0$.
Proof: We have $\Omega = B \cup \bar{B}$, where $B$ and $\bar{B}$ are mutually disjoint.
Hence by the Bay's theorem , $P[B/A]=\frac{P[A/B_k]P[B_k]}{P[A/B]P[B]+P[A/\bar{B}]P[\bar{B}]}, P[B]> 0$.
Example 1: Suppose $B_1, B_2$ and $B_3$ are mutually exclusive events. If $P[B_k]=\frac{1}{3}$ and $P[A/B_k]=\frac{k}{6}$ for $k=1, 2, 3.$ What is $P[A]$?
Solution: By the theorem of total probability, we have
$$ P[A] = \sum_{k=1}^3 P[A/B_k]P[B_k]=\sum_{k=1}^6 \frac{k}{6}\times \frac{1}{3} =\frac{1}{3}$$.
Example 2. The probability that a person can hit the target is 3/5 and the probability that
another person can hit the same target is 2/5. But the first person can fire 8
shots in a given time while the second person fires 10 shots. They fire
together. What is the probability that the second person shoots the target?
Solution:
Let E denote the event of shooting the target, $E_1$ and $E_2$ respectively denote the events that the first person and the second person
shoot the target, we are given
$$ P[E/E_1]=\frac{3}{5}~ and ~ P[E/E_2] = \frac{2}{5}$$
the ratio of the shots of the first person to those of the second person in the same time is $\frac{8}{10}=\frac{4}{5}$. Thus $P[E_1]=\frac{4}{5}P[E_2].$ By Bay's theorem we get
$$P[E_2/E]=\frac{P[E/E_2][P[E_2]}{P[E/E_1]P[E_1]+P[E/E_2]P[E_2]}=\frac{\frac{2}{5} P[E_2]}{\frac{3}{5}\times \frac{4}{5}P[E_2]+\frac{2}{5}P[E_2]}$$
$$P[E_2/E_1]=\frac{5}{11}$$
Example 3. An
urn contains 10 white and three black balls, while another urn contains 3 white
and 5 black balls. Two balls are drawn from the first urn and put into the
second urn and then a ball is drawn from the latter. What is the probability
that it is a white ball?
Solution: The
two balls are drawn from the first urn may be:
(i) both white or (ii) both black or (iii)
one white and one black.
Let these events be denoted by A, B, C
respectively. Then
$$P[A]=\frac{10C_2}{13C_2}=\frac{15}{26}, ~~ P[B]=\frac{3C_2}{13C_2}, ~~ P[C]=\frac{10C_1 3C_1}{13C_2}=\frac{10}{26}$$
(i) 5 white and 5 black balls or (ii) 3 white and 7 black balls or (iii) 4 white and 6 black balls.
Let W denote the event of the drawing a white ball from the second urn in the above three cases. Then
$$P[W/A]=\frac{5}{10}, ~~ P[W/B]=\frac{3}{10},~~ P[W/C]=\frac{4}{10}$$
Hence, $P[W] = P[W/A]P[A]+P[W/B]P[B]+P[W/C]P[C] $
$$=\frac{5}{10}\times \frac{15}{26} + \frac{3}{10}\times\frac{1}{26}+\frac{4}{10}\times\frac{10}{26}=\frac{59}{100}$$
Exercises:
- An urn contains a white and b black balls, while another urn contains c white and d black balls. One ball is transferred from the first urn and put into the second urn and then a ball is drawn from the latter. What is the probability that it will be a white ball?
- Three urns $A_1, A_2, A_3$ contain respectively 3 red, 4 white, 1 blue; 1 red, 2 white, 3 blue; 4 red, 3 white, 2 blue balls. One urn is chosen at random and a ball is withdrawn. It is found to be red. Find the probability that it comes from the urn $A_2$.
- An insurance company insured 2000 scooter drivers, 4000 car drivers, and 6000 truck drivers. The probability of an accident involving a scooter, a car, and a truck are 0.01, 0.03 and 0.15 respectively. One of the insured people meets with an accident. What is the probability that he is a scooter driver?
- In a bolt factory machines A, B, C manufacture respectively 25, 35 and 40 percent of the total. Out of their output 5, 4 and 2 percent ate defective bolts. A bolt is drawn from the produce and is found defective. What is the probabilities that it was manufactured by A, B and C.
- Suppose that in answering a question in a multiple choice test, an examinee knows the answer with probability $p$ or he guesses with probability $1-p $. Assume that the probability of answering a question correctly is unity for an examinee who knows the answer and $1/m$ for the examinee who guesses, where $m$ is the number of multiple-choice alternatives. Show that the probability that an examinee knows the answer to a problem, given that he has correctly answered it, is $\frac{mp}{1+(1-m)p}$
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