Saturday, September 23, 2023

16.9 - Miscellaneous Exercise on Axiomatic Probability

In the previous section, we completed a discussion on axiomatic probability. In this section, we will see some miscellaneous examples.

Solved example 16.14
On her vacations Veena visits four cities (A, B, C and D) in a random order. What is the probability that she visits (i) A before B? (ii) A before B and B before C? (iii) A first and B last? (iv) A either first or second? (v) A just before B?
Solution:
• The first visit can be any one of the four cities.
• The second visit can be any one of the remaining three cities.
• The third visit can be any one of the remaining two cities.
• The fourth visit will be only one remaining city.
• So there are (4 × 3 × 2 × 1) orders in which Veena can visit the four cities. In other words, there are 4! possible orders.
• 4! = 24. So the sample space will contain 24 elements. This is shown below:
S = {
ABCD,    ABDC,    ACBD,    ACDB,    ADBC,    ADCB,
BACD,    BADC,    BCAD,    BCDA,    BDAC,    BDCA,   
CABD,    CADB,    CBAD,    CBDA,    CDAB,    CDBA,
DABC,    DACB,    DBAC,    DBCA,    DCAB,    DCBA
}
• The 24 elements of the sample space can be easily written using the fig.16.3 below:

Fig.16.3

• Since there are 24 elements in S, we can write:
n(S) = 24

Part (i):
1. Let E be the event: She visits A before B.
Let us write the favorable outcomes for E:
• In the first row of S, A comes first in all six cases. So we have 6 from the first row.
• In the second row, B comes first in all six cases. So we have 0 from the second row.
• In the third row, A comes before B in three cases. So we have 3 from the third row.
   ♦ They are: CABD,    CADB,     CDAB
• In the fourth row, A comes before B in three cases. So we have 3 from the third row.
   ♦ They are: DABC,    DACB,     DCAB
• So total number of favorable outcomes =
6 + 0 + 3 + 3 = 12
• We can write: n(E) = 12
2. Since all outcomes in S are equally likely, we get:
$\rm{P(E) = \frac{n(E)}{n(S)} = \frac{12}{24} = \frac{1}{2}}$

Part (ii):
1. Let F be the event: She visits A before B and B before C.
Let us write the favorable outcomes for F:
• In the first row of S, the required order is available in 3 cases. So we have 3 from the first row.
   ♦ They are: ABCD,    ABDC,    ADBC
• In the second row, B comes first in all 6 cases. So we have 0 from the second row.
• In the third row, C comes first in all 6 cases. So we have 0 from the third row.
• In the fourth row, the required order is available in 1 case. So we have 1 from the fourth row.
   ♦ It is: DABC
• So total number of favorable outcomes =
3 + 1 = 4
• We can write: n(F) = 4
2. Since all outcomes in S are equally likely, we get:
$\rm{P(F) = \frac{n(F)}{n(S)} = \frac{4}{24} = \frac{1}{6}}$

Part (iii):
1. Let G be the event: She visits A first and B last.
Let us write the favorable outcomes for G
• In the first row of S, the required order is available in 2 cases. So we have 2 from the first row.
   ♦ They are:  ACDB,    ADCB
• In the second row, B comes first in all 6 cases. So we have 0 from the second row.
• In the third row, C comes first in all 6 cases. So we have 0 from the third row.
• In the fourth row, D comes first in all 6 cases. So we have 0 from the fourth row.
• So total number of favorable outcomes = 2
• We can write: n(G) = 2
2. Since all outcomes in S are equally likely, we get:
$\rm{P(G) = \frac{n(G)}{n(S)} = \frac{2}{24} = \frac{1}{12}}$ 

Part (iv):
1. Let H be the event: She visits A either first or second.
Let us write the favorable outcomes for H
• In the first row of S, the required order is available in all 6 cases. So we have 6 from the first row.
• In the second row, A comes second in 2 cases. So we have 2 from the second row.
   ♦ They are: BACD,    BADC
• In the third row, A comes second in 2 cases. So we have 2 from the third row.
   ♦ They are: CABD,    CADB
• In the fourth row, A comes second in 2 cases. So we have 2 from the fourth row.
   ♦ They are: DABC,    DACB
• So total number of favorable outcomes =
6 + 2 + 2 + 2
• We can write: n(H) = 12
2. Since all outcomes in S are equally likely, we get:
$\rm{P(H) = \frac{n(H)}{n(S)} = \frac{12}{24} = \frac{1}{2}}$

Part (v):
1. Let I be the event: She visits A just before B.
Let us write the favorable outcomes for I
• In the first row of S, the required order is available in 2 cases. So we have 2 from the first row.
   ♦ They are: ABCD,    ABDC
• In the second row, the required order is not available in any of the 6 cases. So we have 0 from the second row.
• In the third row, the required order is available in 2 cases. So we have 2 from the third row.
   ♦ They are: CABD,     CDAB
• In the fourth row, the required order is available in 2 cases. So we have 2 from the fourth row.
   ♦ They are: DABC,     DCAB
• So total number of favorable outcomes =
2 +0 + 2 + 2
• We can write: n(I) = 6
2. Since all outcomes in S are equally likely, we get:
$\rm{P(I) = \frac{n(I)}{n(S)} = \frac{6}{24} = \frac{1}{4}}$

Solved example 16.15
Find the probability that when a hand of 7 cards is drawn from a well shuffled deck of 52 cards, it contains (i) all Kings (ii) 3 Kings (iii) atleast 3 Kings.
Solution:
• Number combinations of 7 cards is $\rm{{}^{52} C_{7}}$.
• So we can write: $\rm{n(S) = {}^{52}C_{7}}$ 

Part (i):
1. We can imagine two boxes.
• The left side box is for the four king cards. The right side box is for the remaining three cards.
• For filling the left side box, we must use only the four king cards. Number of possible combinations of four king cards, taking all four at a time is $\rm{{}^{4} C_{4}}$. So this box  can be filled in $\rm{{}^{4} C_{4}}$ ways.
• For filling the right side box, we must not use any of the four king cards. So the right side box can be filled in $\rm{{}^{48} C_{3}}$ ways.
• The two boxes can be filled together in
$\rm{{}^{4} C_{4}~\times~{}^{48} C_{3}}$ ways.
• Thus the number of combinations with all kings is:
$\rm{{}^{4} C_{4}~\times~{}^{48} C_{3}}$
• We can write:
If A is the event: “getting all the kings”, then:
$\rm{n(A) = {}^{4} C_{4}~\times~{}^{48} C_{3}}$
2. Since all outcomes in S are equally likely, we get:
$\rm{P(A) = \frac{n(A)}{n(S)} = \frac{{}^{4} C_{4}~\times~{}^{48} C_{3}}{{}^{52} C_{7}} = \frac{1}{7735}}$

Part (ii):
1. We can imagine two boxes.
• The left side box is for the three king cards. The right side box is for the remaining four cards.
• For filling the left side box, we must use only the four king cards. Number of possible combinations of four king cards, taking three at a time is $\rm{{}^{4} C_{3}}$. So this box  can be filled in $\rm{{}^{4} C_{3}}$ ways.
• For filling the right side box, we must not use any of the four king cards. Because, the combination must contain exactly three kings. So the right side box can be filled in $\rm{{}^{48} C_{4}}$ ways.
• The two boxes can be filled together in
$\rm{{}^{4} C_{3}~\times~{}^{48} C_{4}}$ ways.
• Thus the number of combinations with all kings is:
$\rm{{}^{4} C_{3}~\times~{}^{48} C_{4}}$
• We can write:
If B is the event: “getting exactly three kings”, then:
$\rm{n(B) = {}^{4} C_{3}~\times~{}^{48} C_{4}}$
2. Since all outcomes in S are equally likely, we get:
$\rm{P(B) = \frac{n(B)}{n(S)} = \frac{{}^{4} C_{3}~\times~{}^{48} C_{4}}{{}^{52} C_{7}} = \frac{9}{1547}}$

Part (iii):
1. Consider the two events A and B that we saw in parts (i) and (ii) respectively above.
   ♦ In A, each combination has exactly 4 kings.
   ♦ In B, each combination has exactly 3 kings.
• So A and B are mutually exclusive events.
2. Consider the set (A∪B).
When the outcome is from (A∪B), two things are possible:
(i) The outcome is from A. Then the condition “atleast 3 kings” is satisfied.
(ii) The outcome is from B. Then also the condition “atleast 3 kings” is satisfied.
3. So we want P(A∪B)
• We have: P(A∪B) = P(A) + P(B)
• Substituting the known values, we get:
P(A∪B) = $\frac{1}{7735} + \frac{9}{1547} = \frac{46}{7735}$

Solved example 16.16
If A, B, C are three events associated with a random experiment, prove that
P(A∪B∪C) = P(A) + P(B) +P(C) − P(A∩B) − P(A∩C) – P(B∩C) + P(A∩B∩C)
Solution:
1. Consider the LHS of the given expression.
• We can think of a new event (B∪C)
• Let E = (B∪C)
2. Now the LHS becomes:

$\begin{array}{ll}
{}&{\rm{P(A∪B∪C)}}
& {~=~}& {\rm{P(A∪E)}} &{} \\

{}&{}
& {~=~}& {\rm{P(A) + P(E) - P(A∩E)}} &{} \\

\end{array}$

3. Next step is to simplify the second term in the RHS of (2). The second term is P(E). We can write:

$\begin{array}{ll}
{}&{\rm{P(E)}}
& {~=~}& {\rm{P(B∪C)}} &{} \\

{}&{}
& {~=~}& {\rm{P(B) + P(C) - P(B∩C)}} &{} \\

\end{array}$

4. Next step is to simplify the third term in the RHS of (2). The third term is P(A∩E). We can write:

$\begin{array}{ll}
{}&{\rm{A∩E}}
& {~=~}& {\rm{A∩(B∪C)}}
&{} \\

{\Rightarrow}&{\rm{A∩E}}
& {~=~}& {\rm{(A∩B)∪(A∩C)~\color{green}{\text{- - - I}}}}
&{} \\

{\Rightarrow}&{\rm{P(A∩E)}}
& {~=~}& {\rm{P \Bigl((A∩B)∪(A∩C) \Bigr)}}
&{} \\

{\Rightarrow}&{\rm{P(A∩E)}}
& {~=~}& {\rm{P(A∩B)~+~P(A∩C)~-~P \Bigl((A∩B)∩(A∩C) \Bigr)}}
&{} \\

{\Rightarrow}&{\rm{P(A∩E)}}
& {~=~}& {\rm{P(A∩B)~+~P(A∩C)~-~P(A∩B∩C)~\color{green}{\text{- - - II}}}}
&{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
In this line we use the “distribution property of intersection of sets over the union”
• Line marked as II:
Here we use the fact that (A∩B)∩(A∩C) = (A∩B∩C)
5. Now we can make the substitutions:
   ♦ From (3), we have the substitute for P(E)  
   ♦ From (4), we have the substitute for P(A∩E)  
• Making these substitutions in (2), we get:
P(A∪B∪C)
= P(A) + P(B) + P(C) - P(B∩C) - P(A∩B) - P(A∩C) + P(A∩B∩C)

Solved example 16.17
In a relay race there are five teams A, B, C, D and E.
(a) What is the probability that A, B and C finish first, second and third, respectively.
(b) What is the probability that A, B and C are first three to finish (in any order)
(Assume that all finishing orders are equally likely)
Solution:
• Five teams can finish in 5! ways.
• So the number of elements in S = 5! = 120
• We can write: n(S) = 120

Part (i):
1. Let G be the event: A, B and C finish first, second and third respectively.
• There are only two possible outcomes for such a finish. They are:
ABC, D, E and ABC, E, D
• So we can write: n(G) = 2
2. Since all outcomes in S are equally likely, we get:
$\rm{P(G) = \frac{n(G)}{n(S)} = \frac{2}{120} = \frac{1}{60}}$
Part (ii):
1. Let H be the event: A, B and C are the first three to finish in any order.
• A, B and C can arrange among themselves in 3! ways.
   ♦ We have, 3! = 3 × 2 = 6
• For each of those 6 ways, D and E can arrange among themselves in 2! ways.
   ♦ We have, 2! = 2 × 1 = 2 ways.
• So the number of favorable outcomes = 6 × 2 = 12
• We can write: n(H) = 12
2. Since all outcomes in S are equally likely, we get:
$\rm{P(H) = \frac{n(H)}{n(S)} = \frac{12}{120} = \frac{1}{10}}$


Link to a few more solved examples is given below:

Miscellaneous Exercise


In the next section, we will see Appendix A.

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Friday, September 15, 2023

16.8 Solved Examples on Axiomatic Probability

In the previous section, we saw how to calculate the Probability of the event "not A". We saw two solved examples also. In this section, we will see two more solved examples.

Solved example 16.12
Two students Anil and Ashima appeared in an examination. The probability that Anil will qualify the examination is 0.05 and that Ashima will qualify the examination is 0.10. The probability that both will qualify the examination is 0.02. Find the probability that
(a) Both Anil and Ashima will not qualify the examination.
(b) Atleast one of them will not qualify the examination and
(c) Only one of them will qualify the examination.
Solution:
First we will write two basic points. We will number them as ๐›ผ and ฮฒ:
Point ๐›ผ:
This can be written in 6 steps:
1. Consider the Venn diagram shown in fig.16.2(a) below. It is already familiar to us.

Fig.6.2

2. (Red ∪ Blue) gives set A.
In our present case, A is the event: Anil qualifies in the examination.
3. (Green ∪ Blue) gives set B.
In our present case, B is the event: Ashima qualifies in the examination.
4. Blue is the set (A∩B).
In our present case, (A∩B) is the event: Both Anil and Ashima qualify in the examination.
5. (Red ∪ Blue ∪ Green) gives the set (A∪B).
In our present case, (A∪B) is the event: Anil or Ashima qualifies in the examination.
6. Consider the portion outside (A∪B), but inside the rectangle. That portion gives the set (A∪B)’. It is the portion shown in yellow color in fig.16.2(b) above.
In our present case, (A∪B)’ is the event: Both Anil and Ashima does not qualify in the examination.

Point ฮฒ:
This can be written in 6 steps:
1. Consider the rectangle S. Imagine that, there are a large number of elements dispersed inside the rectangle. Each of those elements is an outcome.
2. Consider all the outcomes in S. Some of those outcomes are present within (Red ∪ Blue).
• If the outcome of the experiment is from this region, we say that:
Anil has qualified.
• The probability for the outcome to be from this region is given in the question. P(A) = 0.05
3. Consider all the outcomes in S. Some of those outcomes are present within (Green ∪ Blue).
• If the outcome of the experiment is from this region, we say that:
Ashima has qualified.
• The probability for the outcome to be from this region is given in the question. P(B) = 0.1
4. Consider all the outcomes in S. Some of those outcomes are present within Blue.
• If the outcome of the experiment is from this region, we say that:
Both Anil and Ashima have qualified.
• The probability for the outcome to be from this region is given in the question. P(A∩B) = 0.02
5. A and B are not disjoint sets. This can be proved in 3 steps:
(i) If A and B are disjoint sets, (A∩B) = ฮฆ
(ii) Probability of ฮฆ is zero.
(iii) But according to the question, P(A∩B) is not zero. It is 0.02
6. We said that, a large number of outcomes are dispersed within the rectangle S.
• In our present case,
    ♦ we do not know how many such outcomes are there.
    ♦ we do not know the probabilities of each of those outcomes.
• If we knew the number of outcomes and their probabilities, we could calculate P(A), P(B) etc.,
• But as the reader may have already noted, in our present case, we do not need them because, P(A), P(B) and P(A∩B) are already given.  


Now we can answer the questions.
Part (i): Both Anil and Ashima will not qualify.
1. Consider the region (A∪B). It is made up of three regions: red, blue and green.
• If the outcome is from the red region, Anil qualifies. So “Both Anil and Ashima will not qualify” is not satisfied.
• If the outcome is from the blue region, Anil and Ashima qualifies. So “Both Anil and Ashima will not qualify” is not satisfied.
• If the outcome is from the green region, Ashima qualifies. So “Both Anil and Ashima will not qualify” is not satisfied.
• It is clear that, we must discard (A∪B).
2. Consider the region outside (A∪B), but inside the rectangle.
• We know that, such a region is the compliment of set (A∪B). We denote it as (A∪B)’. It is the yellow region of the Venn diagram in fig.16.2(b) above.
• If the outcome is from (A∪B)', we say that:
Both Anil and Ashima will not qualify.
3. The probability for the outcome to be from (A∪B)' is: P(A∪B)'
• So our aim is to find P(A∪B)'
4. It is clear that, (A∪B) and (A∪B)’ are mutually exclusive and exhaustive events.
• We can write:
(A∪B)∪(A∪B)' = S.
• Based on this, we can write the calculations as follows:
$\begin{array}{ll}
{}&{\rm{(A \cup B) \cup (A \cup B)'}}
& {~=~}& {\rm{S}}
&{} \\

{\Rightarrow}&{\rm{P \Bigl((A \cup B) \cup (A \cup B)' \Bigr)}}
& {~=~}& {\rm{P(S)}}
&{} \\

{\Rightarrow}&{\rm{P(A \cup B) + P(A \cup B)'}}
& {~=~}& {\rm{P(S)~\color{green}{\text{- - - I}}}}
&{} \\

{\Rightarrow}&{\rm{P(A) + P(B) - P(A \cap B) + P(A \cup B)'}}
& {~=~}& {\rm{P(S)~\color{green}{\text{- - - II}}}}
&{} \\

{\Rightarrow}&{\rm{0.05 + 0.1 - 0.02 + P(A \cup B)'}}
& {~=~}& {\rm{1}}
&{} \\

{\Rightarrow}&{\rm{0.13 + P(A \cup B)'}}
& {~=~}& {\rm{1}}
&{} \\

{\Rightarrow}&{\rm{P(A \cup B)'}}
& {~=~}& {\rm{1 - 0.13}}
&{} \\

{\Rightarrow}&{\rm{P(A \cup B)'}}
& {~=~}& {\rm{0.87}}
&{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
In this line we use the formula:
P(E∪F) = P(E) + P(F)
Where E and F are disjoint sets.
• Line marked as II:
In this line we use the formula:
P(E∪F) = P(E) + P(F) - P(E∩F)
Where E and F are not disjoint sets.

Part (ii): Atleast one of them will not qualify the examination
1. Consider the blue region.
If the outcome is from this region, it means that both Anil and Ashima qualifies for the examination. So we have to discard this region.
2. Consider the region outside blue but inside the rectangle. This region is (A∩B)’
• This (A∩B)’ is made up of three regions:
(i) Red region (ii) Green region (iii) yellow region.
3. Let us examine each of the three regions.
(i) If the outcome is from the red region, then Anil qualifies but Ashima does not qualify. So “atleast one of them will not qualify” is satisfied.
(ii) If the outcome is from the green region, then Ashima qualifies but Anil does not qualify. So “atleast one of them will not qualify” is satisfied.
(iii) If the outcome is from yellow region, then both do not qualify. So “atleast one of them will not qualify” is satisfied.
4. So (A∩B)’ is our required region.
• The probability for the outcome to be from this region is: P(A∩B)’
5. We have:

$\begin{array}{ll}
{}&{\rm{P(A \cap B)'}}
& {~=~}& {\rm{1 - P(A \cap B)}} &{} \\

{}&{}
& {~=~}& {\rm{1 - 0.02}} &{} \\

{}&{}
& {~=~}& {\rm{0.98}} &{} \\

\end{array}$

Part (iii): Only one of them will qualify the examination.
1. Let us examine each region in fig.16.2 above.
(i) The red region.
If the outcome is from red, then only Anil qualifies. So this region can be considered for our answer.
(ii) The blue region.
If the outcome is from blue, then both qualify. So this region cannot be considered for our answer.
(iii) Green region.
If the outcome is from green, then only Ashima qualifies. So this region can be considered for our answer.
(iv) Yellow region.
If the outcome is from yellow , then neither Anil nor Ashima qualifies. So this region cannot be considered for our answer.
2. Based on the above step, we can write:
The only regions than can be considered are: red and green.
3. We can create a new set: (red ∪ green)
If the outcome is from this union, there are two possibilities:
(i) outcome is from red.
Then only Anil qualifies. So “only one of them will qualify” is satisfied.
(ii) outcome is from green.
Then only Ashima qualifies. So “only one of them will qualify” is satisfied.
4. So (red ∪ green) is our required region.
    ♦ Red is (A-B)
    ♦ Green is (B-A)
• So (A-B)∪(B-A) is our required region.
5. Probability for the outcome to be from this region is: $P \Bigl((A-B) \cup (B-A) \Bigr)$
• (A-B) and (B-A) are disjoint sets. So we can write:
$P \Bigl((A-B) \cup (B-A) \Bigr) = P \Bigl((A-B)\Bigr) + P \Bigl((B-A)\Bigr)$
• So we have to calculate $P \Bigl((A-B)\Bigr) ~\text{and}~ P \Bigl((B-A)\Bigr)$
6. First we will calculate $P \Bigl((A-B)\Bigr)$

$\begin{array}{ll}
{}&{\rm{A}}
& {~=~}& {\rm{(A-B) \cup (A \cap B)~\color{green}{\text{- - - I}}}}
&{} \\

{\Rightarrow}&{\rm{P(A)}}
& {~=~}& {\rm{P \Bigl((A-B) \cup (A \cap B)\Bigr)}}
&{} \\

{\Rightarrow}&{\rm{P(A)}}
& {~=~}& {\rm{P \Bigl((A-B)\Bigr) ~+~ P \Bigl((A \cap B)\Bigr)~\color{green}{\text{- - - II}}}}
&{} \\

{\Rightarrow}&{\rm{0.05}}
& {~=~}& {\rm{P \Bigl((A-B)\Bigr) ~+~ 0.02}}
&{} \\

{\Rightarrow}&{\rm{P \Bigl((A-B)\Bigr)}}
& {~=~}& {\rm{0.05~-~ 0.02}}
&{} \\

{\Rightarrow}&{\rm{P \Bigl((A-B)\Bigr)}}
& {~=~}& {\rm{0.03}}
&{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
Set A is the union of red and blue.
• Line marked as II:
We are able to simply add the individual probabilities because, (A-B) and (A∩B) are disjoint sets.

7. Next we will calculate $P \Bigl((B-A)\Bigr)$

$\begin{array}{ll}
{}&{\rm{B}}
& {~=~}& {\rm{(B-A) \cup (A \cap B)~\color{green}{\text{- - - I}}}}
&{} \\

{\Rightarrow}&{\rm{P(B)}}
& {~=~}& {\rm{P \Bigl((B-A) \cup (A \cap B)\Bigr)}}
&{} \\

{\Rightarrow}&{\rm{P(B)}}
& {~=~}& {\rm{P \Bigl((B-A)\Bigr) ~+~ P \Bigl((A \cap B)\Bigr)~\color{green}{\text{- - - II}}}}
&{} \\

{\Rightarrow}&{\rm{0.1}}
& {~=~}& {\rm{P \Bigl((B-A)\Bigr) ~+~ 0.02}}
&{} \\

{\Rightarrow}&{\rm{P \Bigl((B-A)\Bigr)}}
& {~=~}& {\rm{0.1~-~ 0.02}}
&{} \\

{\Rightarrow}&{\rm{P \Bigl((B-A)\Bigr)}}
& {~=~}& {\rm{0.08}}
&{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
Set B is the union of green and blue.
• Line marked as II:
We are able to simply add the individual probabilities because, (B-A) and (A∩B) are disjoint sets. 

8. Substituting the results from (6) and (7) in (5), we get:
$P \Bigl((A-B) \cup (B-A) \Bigr) = 0.03 + 0.08 = 0.11$


Solved example 16.13
A committee of two persons is selected from two men and two women. What is the probability that the committee will have (a) no man? (b) one man? (c) two men?
Solution:
◼ There are two men (M1 & M2) and two women (W1 & W2)
    ♦ So there is a total of four persons
◼ From that four, two persons can be selected in $\rm{{}^4 C_2}$ ways.
    ♦ So the number of possible outcomes = $\rm{{}^4 C_2}$
    ♦ We can write: n(S) = $\rm{{}^4 C_2}$
    ♦ All the $\rm{{}^4 C_2}$ outcomes are equally likely.
    ♦ Some of those outcomes are: (M1, W1), (W2, M1), etc.,
• Now we can do the calculations:
Part (i):
1. Let A be the event: Getting an outcome with no man.  
2. Since there is to be no man, we must not consider M1 and M2 while making the selections.
• That means, we must consider W1 and W2 only.
• Two women can be selected from two women in $\rm{{}^2 C_2}$ ways.
• So n(A) = $\rm{{}^2 C_2}$
3. Since all outcomes are equally likely, we get:
$\rm{P(A) = \frac{n(A)}{n(S)} = \frac{{}^2 C_2}{{}^4 C_2} = \frac{1}{6}}$

Part (ii):
1. Let B be the event: Getting an outcome with one man.
2. Since there is to be exactly one man, the other person in the committee will be woman.
• One man can be selected from two men in $\rm{{}^2 C_1}$ ways.
• One woman can be selected from two women in $\rm{{}^2 C_1}$ ways.
• Together, they can be selected in $\rm{{}^2 C_1 \times {}^2 C_1}$ ways.
• So n(B) = $\rm{{}^2 C_1 \times {}^2 C_1}$
3. Since all outcomes are equally likely, we get:
$\rm{P(B) = \frac{n(B)}{n(S)} = \frac{{}^2 C_1 \times {}^2 C_1}{{}^4 C_2} = \frac{2 \times 2}{6} = \frac{2}{3}}$

Part (iii):
1. Let C be the event: Getting an outcome with two men.  
2. Since there is to be two men, we must not consider W1 and W2 while making the selections.
• That means, we must consider M1 and M2 only.
• Two men can be selected from two men in $\rm{{}^2 C_2}$ ways.
• So n(C) = $\rm{{}^2 C_2}$
3. Since all outcomes are equally likely, we get:
$\rm{P(C) = \frac{n(C)}{n(S)} = \frac{{}^2 C_2}{{}^4 C_2} = \frac{1}{6}}$


Link to a few more solved examples is given below:

Exercise 16.3


In the next section, we will see some miscellaneous examples.

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Sunday, September 10, 2023

16.7 - Probability of The Event "not A"

In the previous section, we saw how to calculate the Probability of the event "A or B". In this section, we will see probability of the event "not A".

Probability of the event “not A”

This can be explained using an example. It can be written in 5 steps:
1. Consider the following experiment:
A deck of 10 cards is placed on the table. Each card is given a unique number from 1 to 10. One card is drawn at random without looking. If the number on the drawn card is “an even number less than 10”, then event A is said to occur.
2. It is clear that:
   ♦ set S = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
   ♦ set A = {2, 4, 6, 8}
   ♦ set ‘not A’ = A’ = {1, 3, 5, 7, 9, 10}
3. There are 10 possible outcomes.
• If all outcomes are equally likely, the probability of each outcome in S will be 1/10.
4. Now let us calculate the various probabilities:
• Since all outcomes in S are equally likely, we have:
   ♦ $\rm{P(A) = \frac{n(A)}{n(S)} = \frac{4}{10} = \frac{2}{5}}$
   ♦ $\rm{P(A') = \frac{n(A')}{n(S)} = \frac{6}{10} = \frac{3}{5}}$
5. Now we can write an interesting point:
$\rm{1 - P(A)~=~1 - \frac{2}{5}~=~\frac{3}{5}~=~P(A')}$
• That means:
$\rm{1 - P(A)~=~P(A')}$
If we have the probability P(A) of any event A, then we can find P(A') by subtracting it from 1.


We derived the above formula by using an example. We must write the general proof. It can be written in 2 steps:
1. Recall that, union of any set A and it’s compliment will give the universal set.
• In our present case, we can write: A∪A’ = S
2. So we get:
$\begin{array}{ll}
{}&{\rm{A \cup A'}}
& {~=~}& {\rm{S}}
&{} \\

{\Rightarrow}&{\rm{P(A \cup A')}}
& {~=~}& {\rm{P(S)}}
&{} \\

{\Rightarrow}&{\rm{P(A) + P(A') - P(A \cap A')}}
& {~=~}& {\rm{P(S)~\color{green}{\text{- - - I}}}}
&{} \\

{\Rightarrow}&{\rm{P(A) + P(A') - P(\phi)}}
& {~=~}& {\rm{P(S)~\color{green}{\text{- - - II}}}}
&{} \\

{\Rightarrow}&{\rm{P(A) + P(A') - 0}}
& {~=~}& {\rm{P(S)~\color{green}{\text{- - - III}}}}
&{} \\

{\Rightarrow}&{\rm{P(A) + P(A')}}
& {~=~}& {\rm{1~\color{green}{\text{- - - IV}}}}
&{} \\

{\Rightarrow}&{\rm{P(A')}}
& {~=~}& {\rm{1 - P(A)}}
&{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
In this line, we use the formula: P(A∪B) = P(A) + P(B) – P(A∩B)
• Line marked as II:
In this line we apply the fact:
Intersection of any set and it’s compliment will give a null set.
• Line marked as III:
In this line we apply the fact:
Probability of an event with a null set is zero.
• Line marked as IV:
In this line we apply axiom 2: P(S) = 1


Now we will see two solved examples:
Solved example 16.10
One card is drawn from a well shuffled deck of 52 cards. If each outcome is equally likely, calculate the probability that the card will be
(i) a diamond (ii) not an ace
(iii) a black card (i.e., a club or, a spade)
(iv) not a diamond
(v) not a black card.
Solution:
• There are 52 cards. So there are 52 possible outcomes.
• That means set S will have 52 elements. In other words, n(S) = 52.
• Also, all 52 outcomes are equally likely.

Part (i)
:
1. Let A be the event: getting a diamond.
Then set A will have 13 elements. In other words, n(A) = 13
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(A) = \frac{n(A)}{n(S)} = \frac{13}{52} = \frac{1}{4}}$

Alternate method:
1. All 52 outcomes have the same probability which is $\frac{1}{52}$
2. So $\rm{P(A) = 13 \times \frac{1}{52} = \frac{13}{52} = \frac{1}{4}}$ 

Part (ii):
1. Let B be the event: getting an ace.
Then set B will have 4 elements. In other words, n(B) = 4
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(B) = \frac{n(B)}{n(S)} = \frac{4}{52} = \frac{1}{13}}$
3. The event “not ace” can be denoted as “not B”. So we get:
$\rm{P(not~B) = P(B’) = 1 – P(B) = 1 - \frac{1}{13} = \frac{12}{13}}$

Part (iii):
1. Let C be the event: getting a black card.
Then set C will have 26 elements. In other words, n(C) = 26
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(C) = \frac{n(C)}{n(S)} = \frac{26}{52} = \frac{1}{2}}$

Part (iv):
1. Let D be the event: getting a diamond.
Then set D will have 13 elements. In other words, n(D) = 13
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(D) = \frac{n(D)}{n(S)} = \frac{13}{52} = \frac{1}{4}}$
3. The event “not a diamond” can be denoted as “not D”. So we get:
$\rm{P(not D) = P(D’) = 1 – P(D) = 1 - \frac{1}{4} = \frac{3}{4}}$

Part (v)
:
1. Let E be the event: getting a black card.
Then set E will have 26 elements. In other words, n(E) = 26
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(E) = \frac{n(E)}{n(S)} = \frac{26}{52} = \frac{1}{2}}$
3. The event “not getting a black card” can be denoted as “not E”. So we get:
$\rm{P(not~E) = P(E’) = 1 – P(E) = 1 - \frac{1}{2} = \frac{1}{2}}$

Solved example 16.11
A bag contains 9 discs of which 4 are red, 3 are blue and 2 are yellow. The discs are similar in shape and size. A disc is drawn at random from the bag.
Calculate the probability that it will be (i) red, (ii) yellow, (iii) blue, (iv) not blue,
(v) either red or blue.
Solution:
• There are 9 discs. So there are 9 possible outcomes.
• That means set S will have 9 elements. In other words, n(S) = 9.
• Also, all 9 outcomes are equally likely.

Part (i)
:
1. Let R be the event: getting a red disc.
Then set R will have 4 elements. In other words, n(R) = 4
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(R) = \frac{n(R)}{n(S)} = \frac{4}{9}}$

Part (ii):
1. Let Y be the event: getting a yellow disc.
Then set Y will have 2 elements. In other words, n(Y) = 2
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(Y) = \frac{n(Y)}{n(S)} = \frac{2}{9}}$

Part (iii):
1. Let B be the event: getting a blue disc.
Then set B will have 3 elements. In other words, n(B) = 3
2. Given that, all outcomes are equally likely. So we get:
$\rm{P(B) = \frac{n(B)}{n(S)} = \frac{3}{9} = \frac{1}{3}}$

Part (iv):
• The event “not blue” can be denoted as “not B”. So we get:
$\rm{P(not~B) = P(B’) = 1 – P(B) = 1 - \frac{1}{3} = \frac{2}{3}}$

Part (v):
1. The event "either red or blue" is (R∪B)
2. R and B are mutually exclusive events. So we can write:
$\begin{array}{ll}
{}&{\rm{P(R \cup B)}}
& {~=~}& {\rm{P(R) + P(B)}} &{} \\

{}&{}
& {~=~}& {\rm{\frac{4}{9} + \frac{3}{9}}} &{} \\

{}&{}
& {~=~}& {\rm{\frac{7}{9}}} &{} \\

\end{array}$

In the next section, we will see a few more solved examples.

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16.6 - Probability of The Event "A or B"

In the previous section, we saw how to calculate the Probability of an event. In this section, we will see probability of the event "A or B".

Probability of the event “A or B”

• Suppose that, we have two events: Event A and Event B. We have seen that, a new event (A∪B) can be created.
   ♦ If any outcome in A occurs, we say that A∪B has also occurred.
   ♦ If any outcome in B occurs, then also we say that A∪B has also occurred.
• So A∪B indicates “A or B”.
• If we know P(A) and P(B), can we find P(A∪B)?
We have already seen axiom 3 which says:
P(A∪B) = P(A) + P(B)
• But this axiom is valid only if A and B are disjoint sets (mutually exclusive events).
• When A and B are not disjoint sets, we need a method to calculate P(A∪B).

Let us see an example. It can be written in 9 steps:

1. Consider the experiment of tossing a coin thrice.
2. We know that, S = {(H,H,H), (H,H,T), (H,T,H), (H,T,T), (T,H,H), (T,H,T), (T,T,H), (T,T,T)}  
• We see that, there are eight possible outcomes.
3. Next step is to assign probabilities for each of these outcomes.
• Let the probabilities be as shown below:

$\begin{array}{cc}
{\textbf{Outcomes}}&{\textbf{HHH}}
& {\textbf{HHT}}& {\textbf{HTH}} & {\textbf{HTT}} & {\textbf{THH}} & {\textbf{THT}} & {\textbf{TTH}} & {\textbf{TTT}}\\

{\textbf{Probabilities}}&{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}}\\

\end{array}$

• Note that, we put 8 in the denominator because, the total number of outcomes is 8.
• The reader may verify that, the above assigned probabilities satisfy both condition 1 and condition 2. that we saw in the previous section.
(The reader must keep in mind that, 1/8 is only a theoretical value. In actual practice, we will get “values close to 1/8” only when the experiment is repeated a very large number of times)  
4. Suppose that an event A is such that:
A = {HHT, HTH, THH}
• Now we use result 4:
$\rm{P(A)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(A)}}
& {~=~}& {\rm{P \left(\lbrace HHT \rbrace \right) + P \left(\lbrace HTH \rbrace \right) + P \left(\lbrace THH \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}} &{} \\

{}&{}
& {~=~}& {\frac{3}{8}} &{} \\

\end{array}$

5. Suppose that an event B is such that:
B = {HTH, THH, HHH}
• Now we use result 4 again:
$\rm{P(B)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(B)}}
& {~=~}& {\rm{P \left(\lbrace HTH \rbrace \right) + P \left(\lbrace THH \rbrace \right) + P \left(\lbrace HHH \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}} &{} \\

{}&{}
& {~=~}& {\frac{3}{8}} &{} \\

\end{array}$

6. Also we have:
A∪B = {HHT, HTH, THH} ∪ {HTH, THH, HHH}
= {HHT, HTH, THH, HHH}

• Now we use result 4 one more time:
$\rm{P(B)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(A \cup B)}}
& {~=~}& {\rm{P \left(\lbrace HHT \rbrace \right) + P \left(\lbrace HTH \rbrace \right) + P \left(\lbrace THH \rbrace \right) + P \left(\lbrace HHH \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}} &{} \\

{}&{}
& {~=~}& {\frac{4}{8}} &{} \\

{}&{}
& {~=~}& {\frac{1}{2}} &{} \\

\end{array}$

7. Now we can check whether P(A∪B) is equal to [P(A) + P(B)]
   ♦ From (6) we have: P(A∪B) = ½
   ♦ From (4) and (5), we have: P(A) + P(B) = 3/8 + 3/8 = 6/8 = 3/4.
• We see that, they are not equal.
8. So what happened?
Answer can be written in 4 steps:
(i) In our present case, A and B are not disjoint sets.
• Two outcomes appear in both A and B. They are: HTH and THH
(ii) So when we calculate [P(A) + P(B)], the probabilities of those two events will be taken twice.
• But for calculating P(A∪B), we must take the probabilities of each outcome only once.
(iii) So we must make the following deductions from P(A) + P(B):
   ♦ P({HTH}) must be deducted once.
   ♦ P({THH}) must be deducted once.
• That means:
For each element in A∩B, we must deduct it’s probability once.
(iv) Let us write it as a formula:
P(A∪B) = P(A) + P(B) - P(A∩B)
9. Let us check the above formula:
(i) We have: A∩B = {HTH, THH}
(ii) So we get:

$\begin{array}{ll}
{}&{\rm{P(A \cap B)}}
& {~=~}& {\rm{P \left(\lbrace HTH \rbrace \right) + P \left(\lbrace THH \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{8}~+~\frac{1}{8}} &{} \\

{}&{}
& {~=~}& {\frac{1}{4}} &{} \\

\end{array}$

(iii) Substituting the values in the formula, we get:

$\begin{array}{ll}
{}&{\rm{P(A \cup B)}}
& {~=~}& {\rm{P(A)~+~P(B)~-~P(A \cap B)}} &{} \\

{}&{}
& {~=~}& {\frac{3}{8}~+~\frac{3}{8}~-~\frac{1}{4}} &{} \\

{}&{}
& {~=~}& {\frac{4}{8}} &{} \\

{}&{}
& {~=~}& {\frac{1}{2}} &{} \\

\end{array}$

• This is the same result that we obtained in (6). So the formula seems to be working. However, we must write the general proof.


The general proof can be written in 4 steps:
1. Consider the Venn diagram shown below:

Explanation for the Probability of the event "A or B" using Venn diagram.
Fig.16.1

• We see three sets:
   ♦ A-B, (Red color)
   ♦ A∩B (Blue color)
   ♦ B-A (Green color)
• From the diagram, it is clear that:
The three are disjoint sets.
• From the diagram, it also is clear that:
A∪B = (A-B) ∪ (A∩B) ∪ (B-A)
2. Since the three are disjoint sets, we can apply axiom 3 (see section 16.4). We get:
$\begin{array}{ll}
{}&{\rm{P(A \cup B)}}
& {~=~}& {\rm{P \left[(A-B)~ \cup ~ (A \cap B)~ \cup ~(B-A) \right]}} &{} \\

{}&{}
& {~=~}& {\rm{P \left[(A-B)\right]~ \cup ~ P \left[(A \cap B)\right]~ \cup ~P \left[(B-A) \right]}} &{} \\

{}&{}
& {~=~}& {\rm{\left[\sum{P(\{\omega_i\})}~\forall \omega_i \in (A-B)\right]~+~\left[\sum{P(\{\omega_i\})}~\forall \omega_i \in (A \cap B)\right]~+~\left[\sum{P(\{\omega_i\})}~\forall \omega_i \in (B-A)\right]}~\color{green}{\text{- - - I}}} &{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
In this line, there are three terms in the RHS. At a first glance, all three terms may appear to be the same. But on close inspection, we will see the differences:
   ♦ In the first term, we add the probabilities for all elements in (A-B)     
(The symbol ‘∀’ stands for ‘for all’)
   ♦ In the second term, we add the probabilities for all elements in (A∩B)     
   ♦ In the third term, we add the probabilities for all elements in (B-A)

3. In the above step (2), we have derived an important result. We will be using it soon.
• Now we will add the probabilities of events A and B. We get:

$\begin{array}{ll}
{}&{\rm{P(A)~+~P(B)}}
& {~=~}& {\rm{\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in A \Bigr]~+~\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in (B) \Bigr]}} &{} \\

{}&{}
& {~=~}& {\rm{\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in [(A-B) \cup (A \cap B)]\Bigr]~+~\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in [(B-A) \cup (A \cap B)] \Bigr]}~\color{green}{\text{- - - I}}} &{} \\

{}&{}
& {~=~}& {\rm{
\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in (A-B) \Bigr]
~+~\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in  (A \cap B) \Bigr]
~+~\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in (B-A) \Bigr]
~+~\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in  (A \cap B) \Bigr]}~\color{green}{\text{- - - II}}} &{} \\

{}&{}
& {~=~}& {\rm{
P(A \cup B)
~+~\Bigl[\sum{P(\{\omega_i\})}~\forall \omega_i \in  (A \cap B) \Bigr]}~\color{green}{\text{- - - III}}} &{} \\

{}&{}
& {~=~}& {\rm{
P(A \cup B)
~+~P(A \cap B)}} &{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
In this line,
   ♦ We replace A by (A-B)∪(A∩B)
   ♦ We replace B by (B-A)∪(A∩B)
• Line marked as II:
In this line, there are four terms in the RHS.
This is because, each of the two terms in I, is split into two terms.
• Line marked as III:
The first three terms in II, are replaced using the result in (2)

4. Let us write the above result again:
P(A) + P(B) = P(A∪B) + P(A∩B)
• Rearranging the terms, we get:
P(A∪B) = P(A) + P(B) - P(A∩B)

• Thus the formula is proved.


Alternate method:
This can be written in 5 steps:
1. Consider the Venn diagram in fig.16.1 above.
2. Suppose that, we want (A∪B).
• The union of the following two sets will give A∪B:
(i) Set A, which is (red + blue)
(ii) Set B-A, which is green.
• So we can write:
A∪B = A ∪ (B-A)
2. Set A and set (B-A) are disjoint sets. So we can apply axiom 3. We get:
P(A∪B) = P(A) + P(B-A)
3. Now consider set B.
• It is the union of two sets:
(i) Set A∩B, which is blue.
(ii) Set B-A, which is green
• So we can write:
B = (A∩B) ∪ (B-A)
4. Set (A∩B) and set (B-A) are disjoint sets. So we can apply axiom 3. We get:
P(B) = P(A∩B) + P(B-A)
• From this we get:
P(B-A) = P(B) – P(A∩B)
5. Substituting this value of P(B-A) in (2), we get:
P(A∪B) = P(A) + P(B) – P(A∩B)
• Thus the formula is proved.


Let us see an interesting case. It can be written in 3 steps:
1. Consider the two formulas:
(i) P(A∪B) = P(A) + P(B)
• We obtained this formula from axiom 3. We use this formula when A and B are disjoint sets.
(ii) P(A∪B) = P(A) + P(B) – P(A∩B)
• We use this formula when A and B are not disjoint sets.
2. However, the second formula can be used even if A and B are disjoint sets. The reason can be explained in 4 steps:
(i) When A and B are disjoint sets, (A∩B) = ฮฆ.
(ii) Substituting this in the second formula, we get:
P(A∪B) = P(A) + P(B) – P(ฮฆ)
(iii) But we have seen result 1 in a previous section (see section 16.4):
P(ฮฆ) = 0
(iv) So we get:
P(A∪B) = P(A) + P(B) – 0
⇒ P(A∪B) = P(A) + P(B)
3. We can write:
The formula in 1(ii) is valid even if A and B are disjoint sets.


In the next section, we will see Probability of event "not A".

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Wednesday, September 6, 2023

16.5 - Probability of an Event

In the previous section, we saw the basics of the axiomatic approach to probability. In this section, we will see Probability of an event.

Probability of an event

This can be explained using an example. It can be written in 7 steps:
1. Consider the following experiment:
A machine continuously produces pens. Three consecutive pens are examined. Each of those three pens are classified as defective or non-defective.
2. Let us use short forms for defective and non-defective:
• We will put ‘B’ if the pen taken is defective. Here ‘B’ stands for ‘bad’.
• We will put ‘G’ if the pen taken is non-defective. Here ‘G’ stands for ‘good’.
3. So, while examining the three pens, one outcome may be: BBG
• In that case,
   ♦ First pen is bad.
   ♦ Second pen is bad.
   ♦ Third pen is good.
4. In this way, we can write all the possible outcomes:
S = {BBB, BBG, BGB, BGG, GBB, GBG, GGB, GGG}  
• We see that, there are eight possible outcomes.
5. Next step is to assign probabilities for each of these outcomes.
• It is not mentioned that, the machine has any special properties.
• If it has the property of making mostly good pens, then the outcomes like GGG, GBG etc., will get greater probability.
• If it has the property of making mostly bad pens, then the outcomes like BBB, BGB, etc., will get greater probability.
• Since there is no mention of special properties, we will assign equal probabilities as shown below:

$\begin{array}{cc}
{\textbf{Outcomes}}&{\textbf{BBB}}
& {\textbf{BBG}}& {\textbf{BGB}} & {\textbf{BGG}} & {\textbf{GBB}} & {\textbf{GBG}} & {\textbf{GGB}} & {\textbf{GGG}}\\

{\textbf{Probabilities}}&{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}} &{\frac{1}{8}}\\

\end{array}$

• Note that, we put 8 in the denominator because, the total number of outcomes is 8.
• The reader may verify that, the above assigned probabilities satisfy both condition 1 and condition 2. that we saw in the previous section.
(The reader must keep in mind that, 1/8 is only a theoretical value. In actual practice, we will get “values close to 1/8” only when the experiment is repeated a very large number of times)  
6. Suppose that an event A is: “getting exactly one defective pen”.
• Then we get: A = {BGG, GBG, GGB}
• Now we use result 4 that we saw in the previous section:
$\rm{P(A)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(A)}}
& {~=~}& {\rm{P \left(\lbrace BGG \rbrace \right) + P \left(\lbrace GBG \rbrace \right) + P \left(\lbrace GGB \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}} &{} \\

{}&{}
& {~=~}& {\frac{3}{8}} &{} \\

\end{array}$

7. Suppose that an event B is: “getting atleast two defective pen”.
• Then we get: B = {BBB, BBG, BGB, GBB}
• Now we use result 4 that we saw in the previous section:
$\rm{P(A)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(B)}}
& {~=~}& {\rm{P \left(\lbrace BBB \rbrace \right) + P \left(\lbrace BBG \rbrace \right) + P \left(\lbrace BGB \rbrace \right) + P \left(\lbrace GBB \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}~+~\frac{1}{8}} &{} \\

{}&{}
& {~=~}& {\frac{4}{8}} &{} \\

{}&{}
& {~=~}& {\frac{1}{2}} &{} \\

\end{array}$


Let us see another example. It can be written in 5 steps:
1. Consider the experiment of tossing a coin twice.
2. We know that, S = {HH, HT, TH, TT}  
• We see that, there are four possible outcomes.
3. Next step is to assign probabilities for each of these outcomes.
• Let the probabilities be as shown below:

$\begin{array}{cc}
{\textbf{Outcomes}}&{\textbf{HH}}
& {\textbf{HT}}& {\textbf{TH}} & {\textbf{TT}}\\

{\textbf{Probabilities}}&{\frac{1}{4}} &{\frac{1}{7}} &{\frac{2}{7}} &{\frac{9}{28}}\\

\end{array}$

• The reader may verify that, the above assigned probabilities satisfy both condition 1 and condition 2. that we saw in the previous section. 
4. Suppose that an event E is: “getting the same result in both the tosses”.
• Then we get: A = {TT, HH}
• Now we use result 4 that we saw in the previous section:
$\rm{P(A)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(E)}}
& {~=~}& {\rm{P \left(\lbrace HH \rbrace \right) + P \left(\lbrace TT \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{4}~+~\frac{9}{28}} &{} \\

{}&{}
& {~=~}& {\frac{4}{7}} &{} \\

\end{array}$

5. Suppose that an event F is: “getting exactly two heads”.
• Then we get: F = {HH}
• Now we use result 4 that we saw in the previous section:
$\rm{P(A)~=~\sum{P \left(\lbrace \omega_i \rbrace \right)}}$
• So in our present case, we get:

$\begin{array}{ll}
{}&{\rm{P(F)}}
& {~=~}& {\rm{P \left(\lbrace HH \rbrace \right)}} &{} \\

{}&{}
& {~=~}& {\frac{1}{4}} &{} \\

\end{array}$


The above two examples help us to understand how the probability of an event is calculated.


Probabilities of equally likely outcomes

This can be explained using an example. It can be written in 4 steps:
1. Consider the example of pens that we saw above.
• We saw that, all outcomes in S have the same probability.
2. We also saw that: P(A) = 3/8.
• Here the numerator ‘3’ is the number of elements in A. It is the number of outcomes favorable to A.
• Recall that, the number of elements in any set A is denoted as: n(A)
• So we can write:
When the outcomes in S are equally likely, the numerator of the probability for any event A is simply n(A)
• Now consider the denominator ‘8’. It is the number of elements in S.
• So we can write:
When the outcomes in S are equally likely, the denominator of the probability for event A is simply n(S)
3. Similarly, we saw that: P(B) = 1/2 = 4/8.
• Here the numerator ‘4’ is the number of elements in B. It is the number of outcomes favorable to B. It is n(B).
• Now consider the denominator ‘8’. It is n(S).
• Here also, we get the same result:
When the outcomes in S are equally likely, the denominator of the probability for event B is simply n(S)
4. So we can write it as a formula:
• When the outcomes in S are equally likely, the probability of any event A is given by:
$\rm{P(A)~=~\frac{n(A)}{n(S)}}$


The above formula is based on an example. We must write the general proof. It can be written in 3 steps:
1. Let S = {๐œ”1, ๐œ”2, ๐œ”3, . . . , ๐œ”k} be the sample space of an experiment.
• We see that, there are k outcomes. So n(S) = k
• Let us assume that, all those outcomes have the same probability. That is:
P({๐œ”i}) = p, where ๐œ”i ∈ S
2. Using axiom 2, we have:
For any experiment, P(S) = 1
• So for the present case, we can write:
P(S) = p + p + p + . . . + p
(Here p must be written k times because, number of elements in S is k)
• So we get: P(S) = kp = 1
Thus: $p = \frac{1}{k}$.
3. Let A be an event of the experiment. Let the set A have m elements. So n(A) = m.
• Then we can write:
P(A) = p + p + p + . . . + p
(Here p must be written m times because, number of elements in A is m)
• So we get: $\rm{P(A)~=~mp~=~m \times \frac{1}{k}}$
Thus: $\rm{P(A)~=~\frac{m}{k}~=~\frac{n(A)}{n(S)}}$


In the next section, we will see Probability of the event "A or B".

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Tuesday, September 5, 2023

16.4 - Axiomatic Approach to Probability

In the previous section, we saw Exhaustive events. In this section, we will see Axiomatic approach to probability.

• In our earlier classes, we have seen how probability can be calculated in simple cases.
• We calculated probability as a fraction. It was a proper fraction. That is., numerator will be less than the denominator. If a proper fraction is converted to decimal form, the value on the left side of the decimal point will be zero.
• In the denominator, we gave total number of possible outcomes. In the numerator, we gave the total number of favorable outcomes.
• In our present discussion, we will see three axioms developed by the Russian mathematician A.N. Kolmogorov. Those axioms will enable us to calculate probability in complex problems.
Axiom means a statement or rule, which is self-evident. Such a statement/rule can be a useful basis for more advanced mathematical calculations.

Axiom 1: For any event A, P(A) ≥ 0.
• This can be explained in 5 steps:
(i) The probability any event A to occur will be greater than or equal to zero.
    ♦ If P(A) is zero, A will never occur.
    ♦ If P(A) is large, there is a higher probability for A to occur.
    ♦ If P(A) is small, there is a lower probability for A to occur.
(ii) We saw that, probability is always a proper fraction. So the largest possible value for P(A) is 1. When the probability is 1, the event will surely occur.
(iii) Also, the smallest possible value for P(A) is zero. When the probability is zero, the event will never occur.
(iv) Since P(A) is a proper fraction, “zero value” is obtained when the numerator is zero. This happens when there are no favorable outcomes.
(v) Since P(A) is a proper fraction, “1 value” is obtained when the numerator is same as the denominator. This happens when number of favorable outcomes is same as the total number of outcomes.

Axiom 2:
For any experiment, P(S) = 1
• This can be explained in 2 steps:
(i) We know that, any event A can be denoted as a set A. The elements of set A will be the favorable outcomes of event A.
(ii) The sample space of an experiment is also a set. We denote it as S.
• So this S can be considered as an event. Whenever we perform that experiment, we will surely obtain an element of S. Based on this, we can say: Probability for S to occur is 1.

Axiom 3:
If A and B are two mutually exclusive events then P(A∪B) = P(A) + P(B)
• This can be explained using an example. It can be written in 5 steps:
(i) First we will assign the sets.
• Let set S of an experiment have 6 elements.
• Let event A of that experiment have 3 elements.
• Let event B of that experiment have 2 elements.
(ii) Now we will write the probabilities of the individual events:
• We get:
    ♦ P(A) = 3/6 = 1/2
    ♦ P(B) = 2/6 = 1/3
(iii) Given that A and B are disjoint sets. So A∪B will have 5 elements.
• So P(A∪B) = 5/6
(iv) Let us add the individual probabilities:
P(A) + P(B) = 1/2 + 1/3 = 5/6
• This is same as the result in (iii)
(v) Comparing the results in (iii) and (iv), we can write:
P(A∪B) = P(A) + P(B)


Using the above axioms, we can derive four important results.
Result 1:
This can be written in 3 steps:
1. Suppose that, B is a null set. That means, no outcome in S can be used to define the event B.
(For example, when a die is thrown once, the event of “getting number 7” is a null set)
• So we can write: B = ะค
2. Applying axiom 3, we get:
$\begin{array}{ll}
{}&{P(A \cup B)}
& {~=~}& {P(A) + P(B)}
&{} \\

{\Rightarrow}&{P(A \cup \phi)}
& {~=~}& {P(A) + P(\phi)~~ \color {green} {\text{- - - (I)}}}
&{} \\

{\Rightarrow}&{P(A)}
& {~=~}& {P(A) + P(\phi)~~ \color {green} {\text{- - - (II)}}}
&{} \\

{\Rightarrow}&{P(\phi)}
& {~=~}& {0}
&{} \\

\end{array}$

◼ Remarks:
• Line marked as I:
In this line, we are able to apply axiom 3 because, any set A and the null set are disjoint sets
• Line marked as II:
In this line, we use the rule:
Union of any set A with the null set will give A
3. Based on the above calculations, we can write:
If there are no outcomes for an event, then the probability for that event to occur is zero.


Result 2:
This can be written in 4 steps:
1. Let S be the sample space of an experiment.
• Also, let ๐œ”1, ๐œ”2, ๐œ”3, . . . , ๐œ”n be the n elements of S.
• Then we can write: S= {๐œ”1, ๐œ”2, ๐œ”3, . . . , ๐œ”n}
2. So S is the union of n singleton sets:
S = {๐œ”1} ∪ {๐œ”2} ∪ {๐œ”3} ∪ . . . ∪ {๐œ”n}
3. Applying axiom 3, we get:

$\begin{array}{ll}
{}&{\rm{P \left(\lbrace \omega_1 \rbrace \cup \lbrace \omega_2 \rbrace \cup \lbrace \omega_3 \rbrace \cup ~.~.~.~ \cup \lbrace \omega_n \rbrace \right)}}
& {~=~}& {\rm{P \left(\lbrace \omega_1 \rbrace \right) + P \left(\lbrace \omega_2 \rbrace \right) + P \left(\lbrace \omega_3 \rbrace \right) ~+~.~.~.~+~P \left(\lbrace \omega_n \rbrace \right)}}
&{} \\

{\Rightarrow}&{\rm{P(S)}}
& {~=~}& {\rm{P \left(\lbrace \omega_1 \rbrace \right) + P \left(\lbrace \omega_2 \rbrace \right) + P \left(\lbrace \omega_3 \rbrace \right) ~+~.~.~.~+~P \left(\lbrace \omega_n \rbrace \right)}}
&{} \\

{\Rightarrow}&{\rm{1}}
& {~=~}& {\rm{P \left(\lbrace \omega_1 \rbrace \right) + P \left(\lbrace \omega_2 \rbrace \right) + P \left(\lbrace \omega_3 \rbrace \right) ~+~.~.~.~+~P \left(\lbrace \omega_n \rbrace \right)}}
&{} \\

\end{array}$

◼ Remarks:
From axiom 2, we know that: P(S) = 1

4. So we can write:
Sum of the individual probabilities of all individual outcomes of an experiment will be 1.


Result 3:
This can be written in 5 steps:
1. From result 2, we know that:
P({๐œ”1}) + P({๐œ”2}) + P({๐œ”3}) + . . . + P({๐œ”n}) = 1
• Note that, the sum on the RHS is 1.
2. Each term on the LHS is a proper fraction.
• Since the sum is 1, each term must be either less than 1 or equal to 1.
3. But axiom 1 tells us that:
Each term will be either greater than zero or equal to zero.
4. Combining (2) and (3), we get:
0 ≤ Any term ≤ 1
• Mathematically, we write this as:
0 ≤ P({๐œ”i}) ≤ 1
5. So we can write:
Take any outcome from S. That outcome can be considered as a simple event (event with only one outcome). The probability of that event will be greater than or equal to zero and at the same time, less than or equal to 1.


Result 4:
This can be written in 4 steps:
1. Let A be an event in an experiment.
• Also, let ๐œ”1, ๐œ”2, ๐œ”3, . . . , ๐œ”n be the n elements of A.
• Then we can write: A = {๐œ”1, ๐œ”2, ๐œ”3, . . . , ๐œ”n}
2. So A is the union of n singleton sets:
A = {๐œ”1} ∪ {๐œ”2} ∪ {๐œ”3} ∪ . . . ∪ {๐œ”n}
3. Applying axiom 3, we get:

$\begin{array}{ll}
{}&{\rm{P \left(\lbrace \omega_1 \rbrace \cup \lbrace \omega_2 \rbrace \cup \lbrace \omega_3 \rbrace \cup ~.~.~.~ \cup \lbrace \omega_n \rbrace \right)}}
& {~=~}& {\rm{P \left(\lbrace \omega_1 \rbrace \right) + P \left(\lbrace \omega_2 \rbrace \right) + P \left(\lbrace \omega_3 \rbrace \right) ~+~.~.~.~+~P \left(\lbrace \omega_n \rbrace \right)}}
&{} \\

{\Rightarrow}&{\rm{P(A)}}
& {~=~}& {\rm{P \left(\lbrace \omega_1 \rbrace \right) + P \left(\lbrace \omega_2 \rbrace \right) + P \left(\lbrace \omega_3 \rbrace \right) ~+~.~.~.~+~P \left(\lbrace \omega_n \rbrace \right)}}
&{} \\

{\Rightarrow}&{\rm{P(A)}}
& {~=~}& {\rm{\sum{P \left(\lbrace \omega_i \rbrace \right)}}}
&{} \\

\end{array}$

4. So we can write:
Sum of the individual probabilities of all individual outcomes of an event will be the probability of that event.


◼ Based on the three axioms and four results, we can write:
Any experiment must satisfy two conditions:
Condition 1:
Take any individual outcome of an experiment. The probability of that outcome will be greater than or equal to zero, but at the same time less than or equal to 1.

• Mathematically, we can write this as:
$\rm{0 \le P(\lbrace \omega_i\rbrace) \le 1,~Where~ \omega_i \in S}$
Condition 2:
Take the sum of the probabilities of all individual outcomes in S. That sum will be 1.

• Mathematically, we can write this as:
$\rm{\sum{P(\lbrace \omega_i\rbrace)} = 1,~Where~ \omega_i \in S}$

• Let us see an example. It can be written in steps:
1. Consider the experiment of tossing a coin once.
We know that, S = {H,T}
2. Let us assign individual probabilities for the two outcomes:
    ♦ P({H}) = ½
    ♦ P({T}) = ½
3. Does the above probability values satisfy the two conditions?
(i) We have: 0 ≤ ½ ≤ 1
So condition 1 is satisfied.
(ii) We have: (½ + ½) = 1
So condition 2 is also satisfied.
4. We know that exact ½ cannot be obtained even if we repeat the experiment 50000 times. What if we repeat the experiment only 20 times and get the following values?
    ♦ P({H}) = 1/4
    ♦ P({T}) = 3/4
5. Does the above probability values satisfy the two conditions?
(i) We have:
    ♦ 0 ≤ 1/4 ≤ 1
    ♦ 0 ≤ 3/4 ≤ 1
So condition 1 is satisfied.
(ii) We have: (1/4 + 3/4) = 1
So condition 2 is also satisfied.
6. We see that ¼ and ¾ also satisfy the two conditions.
• In fact, there are infinite number of possible values such that:
    ♦ P({H}) = p
    ♦ P({T}) = (1- p)
Where 0 ≤ p ≤ 1


Now we will see a solved example:

Solved example 16.9
Let a sample space be S = {๐œ”1, ๐œ”2, ๐œ”3, . . . , ๐œ”6}. Which of the following assignments of probabilities to each outcome are valid ?

$\begin{array}{cc}
{\textbf{Outcomes}}&{\mathbf{\omega_1}}
& {\mathbf{\omega_2}}& {\mathbf{\omega_3}}
&{\mathbf{\omega_4}} &{\mathbf{\omega_5}} &{\mathbf{\omega_6}}\\

{\textbf{(a)}}&{\frac{1}{6}}
& {\frac{1}{6}}& {\frac{1}{6}}
&{\frac{1}{6}} &{\frac{1}{6}} &{\frac{1}{6}}\\

{\textbf{(b)}}&{1}
& {0}& {0}
&{0} &{0} &{0}\\

{\textbf{(c)}}&{\frac{1}{8}}
& {\frac{2}{3}}& {\frac{1}{3}}
&{\frac{1}{3}} &{- \frac{1}{4}} &{- \frac{1}{3}}\\

{\textbf{(d)}}&{\frac{1}{12}}
& {\frac{1}{12}}& {\frac{1}{6}}
&{\frac{1}{6}} &{\frac{1}{6}} &{\frac{3}{2}}\\

{\textbf{(e)}}&{0.1}
& {0.2}& {0.3}
&{0.4} &{0.5} &{0.6}\\

\end{array}$

Solution:
We have two conditions to check whether the probabilities assigned to the outcomes are valid.
Condition 1:
$\rm{0 \le P(\lbrace \omega_i\rbrace) \le 1,~Where~ \omega_i \in S}$
Condition 2:
$\rm{\sum{P(\lbrace \omega_i\rbrace)} = 1,~Where~ \omega_i \in S}$

Let us check each case:
Part (a):
• Condition 1 is satisfied because, all probability values are greater than or equal to 0 and at the same time, less than or equal to 1.
• Condition 2 is satisfied because:
$\frac{1}{6} + \frac{1}{6} + \frac{1}{6} + \frac{1}{6} + \frac{1}{6} + \frac{1}{6}~=~1$
• Both conditions are satisfied. So the assigned probabilities are valid.

Part (b):
• Condition 1 is satisfied because, all probability values are greater than or equal to 0 and at the same time, less than or equal to 1.
• Condition 2 is satisfied because:
1 + 0 + 0 + 0 + 0 + 0 = 1
• Both conditions are satisfied. So the assigned probabilities are valid.

Part (c):
• Condition 1 is not satisfied because, P({๐œ”5}) and P({๐œ”6}) are less than 0.
• Since condition 1 is not satisfied, there is no need to check condition 2.
• Condition 1 is not satisfied. So the assigned probabilities are not valid.

Part (d):
• Condition 1 is not satisfied because, P({๐œ”6}) is greater than 1.
• Since condition 1 is not satisfied, there is no need to check condition 2.
• Condition 1 is not satisfied. So the assigned probabilities are not valid.

Part (e):
• Condition 1 is satisfied because, all probability values are greater than or equal to 0 and at the same time, less than or equal to 1.
• Condition 2 is not satisfied because:
0.1 + 0.2 + 0.3 + 0.4 + 0.5 + 0.6 = 2.1 ≠ 1
• Condition 2 is not satisfied. So the assigned probabilities are not valid.


In the next section, we will see Probability of an event.

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