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Set Theoretical Notations of Probability

Set Theoretical Notations of Probability

Edited By Komal Miglani | Updated on Jul 02, 2025 07:54 PM IST

Set theoretical notations provide a clear way to represent events and their relationship These operations on the events show us how the values are related to each other. Fundamental operations are union, intersection, and complement. These operations can help analysts to predict the likelihood of an event.

Set Theoretical Notations of Probability
Set Theoretical Notations of Probability

Set Theoretical Notations of Probability

A set is simply a collection of distinct objects, considered as a whole. These objects, called elements or members of the set, can be anything: numbers, people, letters, etc. Sets are particularly useful in defining and working with groups of objects that share common properties.

An experiment is called a random experiment.

1. A possible result of a random experiment is called its outcome and the set of all possible outcomes of a random experiment is called Sample Space. Generally, sample space is denoted by $S$.

2. The set of outcomes from an experiment is known as an Event.

3. Two or more than two events are said to be mutually exclusive if the occurrence of one of the events excludes the occurrence of the other.

4. If an event has only one sample point of a sample space, it is called a simple (or elementary) event.

Sets Operations:

1. Union: The union of $A$ and $B$ is the set that consists of all the elements of A and all the elements of B, the common elements being taken only once. The symbol $'∪’$ is used to denote the union. Symbolically, we write $A U B = \{x: x ∈ A or x ∈ B\}.$

2. Intersection: The intersection of sets A and B is the set of all elements that are common to both A and B. The symbol $'∩'$ is used to denote the intersection. Symbolically, we write $A ∩ B = \{x: x ∈ A and x ∈ B\}$

3. Difference: The difference of the sets $A$ and $B$ in this order is the set of elements that belong to $A$ but not to $B$. Symbolically, we write $A – B$ and read as “$A$ minus $B$”.

4. Complement: Let $U$ be the universal set and $A$ is a subset of $U$. Then the complement of $A4$ is the set of all elements of U which are not the elements of $A$. Symbolically, we use A' or Ac to denote the complement of $A$ with respect to $U$. $A' = \{x∶ x ∈ U$ and $x ∉ A \}$. Obviously, $A' = U – A$

5. Cartesian Product: The Cartesian product of two sets $A$ and $B$, denoted by $A×B$ is the set of all ordered pairs $(a,b)$ where $a$ is an element of $A$ and $b$ is an element of $B$.

The Addition Rule of Probability

(Probability of the event $'A$ or $B'$ )

If $A$ and $B$ are any two events defined on a sample space, then the probability of occurrence of at least one of the events $A$ and $B$ is $P(A ∪ B)$ and it equals $P(A) + P(B) - P(A ∩ B)$.

From the set theory, we know that

$
\mathrm{n}(\mathrm{A} \cup \mathrm{B})=\mathrm{n}(\mathrm{A})+\mathrm{n}(\mathrm{B})-\mathrm{n}(\mathrm{A} \cap \mathrm{B})
$

Divide by $n(S)$ both side

$
\begin{aligned}
& \frac{n(A \cup B)}{n(S)}=\frac{n(A)}{n(S)}+\frac{n(B)}{n(S)}-\frac{n(A \cap B)}{n(S)} \\
& P(A \cup B)=P(A)+P(B)-P(A \cap B)
\end{aligned}
$

Special case

If $A$ and $B$ are disjoint sets, i.e., they are mutually exclusive events, then $A ∩ B = φ.$ Therefore $P(A ∩ B) = P (φ) = 0$

Thus, for mutually exclusive events $A$ and $B$, we have $P(A ∪ B) = P(A) + P(B), $

If $A$ and $B$ are two events, then

$ (A - B) ∩ (A ∩ B) = φ$ and $ A = (A - B) ∪ (A ∩ B) $

So, $ P(A) = P(A - B) + P(A ∩ B) - 0$

$= P(A ∩ B’) + P(A ∩ B) $

or $P(A) - P(A ∩ B) = P(A ∩ B’) = P(A - B)$ $[∵ A - B = A ∩ B’ ]$

Similarly, $P(B) - P(A ∩ B) = P(B ∩ A’) = P(B - A) $

If $A, B$ and $C$ are any three events in a sample space $S$, then

$P(A ∪ B ∪ C) = P(A) + P(B) + P(C) - P(A ∩ B) - P(A ∩ C) - P(B ∩ C) + P(A ∩ B ∩ C)$

If $A, B$ and $C$ are any three mutually exclusive events in a sample space $S$, then

$P(A ∪ B ∪ C) = P(A) + P(B) + P(C)$


Probability of event ‘not A’ or Complementary Event

If $E$ is the any event and $E’$ be the complement of the event $E$. Since, $E$ and $E’$ are disjoint and exhaustive sets.

$\begin{array}{ll} & \mathrm{E} \cup \mathrm{E}^{\prime}=S \\ \therefore & \mathrm{n}\left(\mathrm{E} \cup \mathrm{E}^{\prime}\right)=\mathrm{n}(\mathrm{S}) \\ \Rightarrow & \mathrm{n}(\mathrm{E})+\mathrm{n}\left(\mathrm{E}^{\prime}\right)=\mathrm{n}(\mathrm{S}) \\ \Rightarrow \quad & \frac{\mathrm{n}(\mathrm{E})}{\mathrm{n}(\mathrm{S})}+\frac{\mathrm{n}\left(\mathrm{E}^{\prime}\right)}{\mathrm{n}(\mathrm{S})}=1 \\ \Rightarrow \quad & \mathrm{P}(\mathrm{E})+\mathrm{P}\left(\mathrm{E}^{\prime}\right)=1 \\ \text { or } & \mathrm{P}(\mathrm{E})=1-\mathrm{P}(\text { not } \mathrm{E})=1-\mathrm{P}\left(\mathrm{E}^{\prime}\right)\end{array}$

Recommended Video Based on Set Theoretical Notations


Solved Examples Based on Set Theoretical Notations:

Example 1: Two dice are thrown. What is the probability that the sum of the numbers on the two dice is eight?

1) $\frac{5}{36}$
2) $\frac{5}{18}$
3) $\frac{1}{4}$
4) $\frac{1}{3}$

Solution

Favourable Cases

$
\begin{aligned}
& =\{(6,2),(2,6),(5,3),(3,5),(4,4) \\
& =5
\end{aligned}
$

Total Cases
$
\begin{aligned}
=36=\{ & (1,1),(1,2),(1,3) \\
& (2,1),(2,2), \ldots(6,6)\}
\end{aligned}
$

So, required probability

$
=\frac{5}{36}
$

Hence, the answer is the option(1).

Example 2: In a class of $200$ students, $125$ students have taken a programming language course, $85$ students have taken a data structures course, $65$ students have taken a computer organization course, $50$ students have taken both programming languages and data: structures, $35$ students have taken both programming languages and computing organization, $30$ students have taken both data structures and computer organization, $15$ students have taken all the three courses. How many students have not taken any of the three courses?

1) $15$

2) $20$

3) $25$

4) $35$

Solution

Method I :

Using Venn diagram we can easily see that Number of students have not taken any of the three subjects $=25$

Method II :

Let $\mathrm{p}=$ programming language, $\mathrm{D}=$ Data structure, $\& \mathrm{C}=$ computer organization

Given

$
\begin{aligned}
& \mathrm{p}(\mathrm{P})=\frac{125}{200}, \mathrm{p}(\mathrm{D})=\frac{85}{200}, \mathrm{p}(\mathrm{C})=\frac{65}{200} \\
& \mathrm{p}(\mathrm{P} \cap \mathrm{D})=\frac{50}{200}, \mathrm{p}(\mathrm{P} \cap \mathrm{C})=\frac{35}{200} \\
& \mathrm{p}(\mathrm{D} \cap \mathrm{C})=\frac{30}{200}, \mathrm{p}(\mathrm{P} \cap \mathrm{D} \cap \mathrm{C})=\frac{15}{200}
\end{aligned}
$


Using Addition theorem,

$
\begin{aligned}
\mathrm{p}(\mathrm{P} \cup \mathrm{D} \cup \mathrm{C}) & =p(\mathrm{P})+\mathrm{p}(\mathrm{D})+\mathrm{p}(\mathrm{C})-\mathrm{p}(\mathrm{P} \cap \mathrm{D}) \\
- & \mathrm{p}(\mathrm{D} \cap \mathrm{C})-\mathrm{p}(\mathrm{P} \cap \mathrm{C})+\mathrm{p}(\mathrm{P} \cap \mathrm{D} \cap \mathrm{C}) \\
& =\frac{7}{8} \\
\Rightarrow \mathrm{P}(\overline{\mathrm{P}} \cap \overline{\mathrm{D}} \cap \overline{\mathrm{C}})= & 1-\mathrm{P}(\mathrm{P} \cup \mathrm{D} \cup \mathrm{C})
\end{aligned}
$

$
=1-\frac{7}{8}=\frac{1}{8}
$

Hence, Number of students who had not taken any of the three courses

$
=\frac{1}{8} \times 200=25
$

Example 3: Two coins are simultaneously tossed. The probability of two heads simultaneously appearing
1) $\frac{1}{8}$
2) $\frac{1}{6}$
3) $\frac{1}{4}$
4) $\frac{1}{2}$

Solution

Set of possible outcomes

$
\begin{aligned}
& \qquad=\{(\mathrm{HH}),(\mathrm{HT}),(\mathrm{TH}),(\mathrm{TT})\} \\
& \text { Set of favorable outcomes }=\{(\mathrm{HH})\} \\
& \therefore \text { The required probability }=\frac{1}{4}
\end{aligned}
$

Hence, the answer is the option (3).
Example 4:
A fair coin is tossed independently four times. the probability of the event "the number of times heads show up is more than the number of times tails show up is
1) $\frac{1}{16}$
2) $\frac{1}{8}$
3) $\frac{1}{4}$
4) $\frac{5}{16}$

Solution
$
\mathrm{n}(\mathrm{s})=16
$

Favorable cases $=\{ HHHT, HHTH, HTHH, THHH, HHHH \}$
No of favorable cases $=5$
Required Probability $=\frac{5}{16}$
Hence, the answer is the option (4).

Example 5: The probability that a given positive integer lying between 1 and 100 (both inclusive) is NOT divisible by $2$, $3$ , or $5$ is
1) $0.26$
2) $0.36$
3) $0.21$
4) $0.23$

Solution

Number of integers in the set which are divisible by

$
\begin{aligned}
& 2 \text { or } 3 \text { or } 5= \\
& \begin{aligned}
\mathrm{n}(2 \cup 3 \cup 5)= & \mathrm{n}(2)+\mathrm{n}(3)+\mathrm{n}(5)-\mathrm{n}(2 \cap 3) \\
& -\mathrm{n}(3 \cap 5)-\mathrm{n}(5 \cap 2)+\mathrm{n}(2 \cap 3 \cap 5) \\
& =50+33+20-16-10-6+3 \\
& =74
\end{aligned}
\end{aligned}
$

$\therefore$ The number of integers between 1 and 100 which are not divisible by 2 or 3 or $5=n(\overline{2} \cap \overline{3} \cap \overline{5})$

$
\begin{aligned}
& \text { The number of integers between } 1 \text { and } 100 \text { which are not divisible by } 2 \text { or } 3 \text { or } 5=\mathrm{n}(\overline{2} \cap \overline{3} \cap \overline{5}) \\
& \qquad=100-74 \\
& =26
\end{aligned}
$

$\therefore$ Req. probability

$
=\frac{26}{100}=0.26
$

Frequently Asked Questions (FAQs)

1. What is a set?

A set is simply a collection of distinct objects, considered as a whole. These objects, called elements or members of the set, can be anything: numbers, people, letters, etc.

2. What is a simple event?

If an event has only one sample point of a sample space, it is called a simple (or elementary) event.

3. What is mutually exclusive events?

Two or more than two events are said to be mutually exclusive if the occurrence of one of the events excludes the occurrence of the other.

4. What is a sample space?

The set of all possible outcomes of a random experiment is called Sample Space.

5. What are the set operations?

The set operations are union, intersection, difference, complement and cartesian product.

6. What is the universal set in probability theory?
The universal set, often denoted by Ω (omega), is the set of all possible outcomes in a probability experiment. It represents the entire sample space, containing every possible event that could occur in a given scenario.
7. How is the empty set represented in set notation for probability?
The empty set, also called the null set, is represented by ∅ or {}. In probability, it represents an impossible event or an event with no outcomes.
8. What does A ∪ B mean in probability set notation?
A ∪ B represents the union of sets A and B. It includes all elements that are in set A, set B, or both sets. In probability, this notation is used to describe the event where either A or B (or both) occur.
9. How is set intersection represented in probability notation?
Set intersection is represented by ∩. For example, A ∩ B means the intersection of sets A and B, which includes all elements that are common to both sets. In probability, this represents the event where both A and B occur simultaneously.
10. What does A' (or Ac) mean in set notation for probability?
A' or Ac represents the complement of set A. It includes all elements in the universal set that are not in A. In probability, this notation is used to describe the event where A does not occur.
11. What does P(A | B) = P(A | B') imply about events A and B?
If P(A | B) = P(A | B'), it implies that events A and B are independent. The occurrence or non-occurrence of B does not affect the probability of A.
12. What does P(A ∩ B) = P(A) × P(B | A) represent in probability theory?
This equation represents the multiplication rule of probability. It shows how to calculate the probability of both A and B occurring by multiplying the probability of A with the conditional probability of B given A.
13. What does P(A | B) = P(A) imply about events A and B?
If P(A | B) = P(A), it implies that events A and B are independent. The probability of A occurring is not affected by whether B has occurred or not.
14. What does P(A | B) = P(A) / P(B) × P(B | A) represent?
This equation is another form of Bayes' theorem. It shows how to calculate the conditional probability of A given B using the probabilities of A, B, and B given A.
15. How is set difference represented in probability set notation?
Set difference is represented by the symbol \. For example, A \ B (read as "A minus B") includes all elements that are in set A but not in set B. In probability, this notation describes the event where A occurs but B does not.
16. What is the meaning of P(A | B) in probability notation?
P(A | B) represents the conditional probability of event A given that event B has occurred. It reads as "the probability of A given B" and measures the likelihood of A happening, knowing that B has already happened.
17. What does P(Ω) represent in probability theory?
P(Ω) represents the probability of the universal set Ω. In probability theory, this is always equal to 1, as the universal set contains all possible outcomes, and one of these outcomes must occur.
18. What does P(A') + P(A) equal, and why?
P(A') + P(A) always equals 1. This is because A and its complement A' are mutually exclusive and exhaustive events, meaning they cover all possibilities in the sample space.
19. What does P(A ∪ B ∪ C) represent, and how is it calculated?
P(A ∪ B ∪ C) represents the probability that at least one of the events A, B, or C occurs. It's calculated using the inclusion-exclusion principle: P(A ∪ B ∪ C) = P(A) + P(B) + P(C) - P(A ∩ B) - P(A ∩ C) - P(B ∩ C) + P(A ∩ B ∩ C).
20. What does P(A ∩ B) = 0 imply about events A and B?
If P(A ∩ B) = 0, it implies that events A and B are mutually exclusive. They cannot occur simultaneously, and their intersection is the empty set.
21. What does P(A | B) + P(A' | B) = 1 represent in probability theory?
This equation represents the fact that, given B has occurred, either A or its complement A' must occur. It's an application of the law of total probability to conditional probabilities.
22. What does P(A | B) × P(B | C) = P(A ∩ B | C) represent?
This equation represents the chain rule of probability. It shows how to calculate the conditional probability of two events A and B occurring, given that a third event C has occurred.
23. What does P(A | B) ≠ P(B | A) generally imply about events A and B?
If P(A | B) ≠ P(B | A), it generally implies that events A and B are not independent. The probability of A given B is not the same as the probability of B given A.
24. How is the concept of a marginal probability distribution derived from a joint distribution in set notation?
For discrete random variables X and Y with joint distribution P(X = x, Y = y), the marginal distribution of X is P(X = x) = ΣyP(X = x, Y = y). For continuous random variables with joint density f(x,y), the marginal density of X is f(x) = ∫ f(x,y)dy.
25. What does P(A) mean in probability notation?
P(A) represents the probability of event A occurring. It is a measure of the likelihood of event A happening, expressed as a number between 0 and 1 (inclusive).
26. How is the probability of the union of two events written in set notation?
The probability of the union of two events A and B is written as P(A ∪ B). This represents the probability that either event A or event B (or both) will occur.
27. What does P(A ∩ B) represent in probability?
P(A ∩ B) represents the probability of the intersection of events A and B. It is the probability that both events A and B occur simultaneously.
28. How is the probability of the complement of an event written?
The probability of the complement of an event A is written as P(A') or P(Ac). It represents the probability that event A does not occur.
29. How is the probability of mutually exclusive events represented?
For mutually exclusive events A and B, their intersection is empty (A ∩ B = ∅), and the probability of their union is the sum of their individual probabilities: P(A ∪ B) = P(A) + P(B).
30. What does {x | x ∈ A} mean in set builder notation?
This notation reads as "the set of all x such that x is an element of set A". It's a way of defining a set by describing the properties of its elements. In probability, it's often used to specify subsets of the sample space.
31. How is the probability of the empty set expressed?
The probability of the empty set is always 0, written as P(∅) = 0. This means that an impossible event (represented by the empty set) has zero probability of occurring.
32. How is the sum rule of probability expressed in set notation?
The sum rule of probability for two events A and B is expressed as P(A ∪ B) = P(A) + P(B) - P(A ∩ B). This accounts for the possibility of double-counting events that are in both A and B.
33. What does A ⊆ B mean in set notation, and how does it relate to probability?
A ⊆ B means that A is a subset of B, or every element in A is also in B. In probability, if A ⊆ B, then P(A) ≤ P(B), meaning the probability of A occurring is less than or equal to the probability of B occurring.
34. How is the product rule of probability expressed in set notation?
The product rule of probability for independent events A and B is expressed as P(A ∩ B) = P(A) × P(B). For dependent events, it's P(A ∩ B) = P(A) × P(B|A) or P(B) × P(A|B).
35. How is De Morgan's law expressed in set notation for probability?
De Morgan's laws in set notation are (A ∪ B)' = A' ∩ B' and (A ∩ B)' = A' ∪ B'. In probability, these translate to P((A ∪ B)') = P(A' ∩ B') and P((A ∩ B)') = P(A' ∪ B').
36. What does P(A - B) represent in probability?
P(A - B) represents the probability of the set difference between A and B. It's the probability that event A occurs but event B does not. It can be calculated as P(A - B) = P(A) - P(A ∩ B).
37. How is the law of total probability expressed using set notation?
The law of total probability states that for a partition of the sample space into events B1, B2, ..., Bn, the probability of an event A is P(A) = P(A ∩ B1) + P(A ∩ B2) + ... + P(A ∩ Bn).
38. How is the concept of disjoint events represented in set notation?
Disjoint events, also called mutually exclusive events, are represented as A ∩ B = ∅. This means that events A and B cannot occur simultaneously, and their intersection is the empty set.
39. What does P(A | B) × P(B) = P(B | A) × P(A) represent?
This equation represents Bayes' theorem, a fundamental concept in probability theory. It shows the relationship between the conditional probabilities of A given B and B given A, allowing us to update probabilities based on new information.
40. How is the concept of independence expressed in probability set notation?
Two events A and B are independent if P(A ∩ B) = P(A) × P(B). This means that the occurrence of one event does not affect the probability of the other event occurring.
41. How is the concept of a partition of a sample space expressed in set notation?
A partition of a sample space Ω is a collection of subsets {B1, B2, ..., Bn} such that: (1) Bi ∩ Bj = ∅ for i ≠ j (mutually exclusive), and (2) B1 ∪ B2 ∪ ... ∪ Bn = Ω (collectively exhaustive).
42. What does P(A ∩ B) ≤ min{P(A), P(B)} mean in probability theory?
This inequality states that the probability of both A and B occurring is always less than or equal to the probability of the less likely event. It's a consequence of the fact that the intersection of two sets is always a subset of each individual set.
43. How is the concept of conditional probability expressed using set notation?
Conditional probability is expressed as P(A|B) = P(A ∩ B) / P(B), where P(B) > 0. This formula shows how to calculate the probability of A occurring, given that B has already occurred.
44. What does P(A ∪ B) ≥ max{P(A), P(B)} mean in probability theory?
This inequality states that the probability of either A or B (or both) occurring is always greater than or equal to the probability of the more likely event. It's because the union includes all outcomes from both events.
45. How is the concept of a sample space represented in set notation?
The sample space, often denoted by Ω or S, is the set of all possible outcomes in a probability experiment. In set notation, it's represented as Ω = {all possible outcomes}.
46. How is the concept of a power set used in probability theory?
The power set of a sample space Ω, denoted by P(Ω), is the set of all possible subsets of Ω, including Ω itself and the empty set. In probability theory, events are elements of the power set.
47. How is the concept of a σ-algebra represented in set notation for probability?
A σ-algebra F on a sample space Ω is a collection of subsets of Ω that satisfies three conditions: (1) Ω ∈ F, (2) If A ∈ F, then A' ∈ F, and (3) If A1, A2, ... ∈ F, then their union ∪Ai ∈ F.
48. What does P(A ∪ B) = P(A) + P(B) - P(A ∩ B) represent?
This equation represents the addition rule of probability. It calculates the probability of either A or B occurring by adding their individual probabilities and subtracting the probability of their intersection to avoid double-counting.
49. How is the concept of a probability measure defined using set notation?
A probability measure P on a σ-algebra F is a function P: F → [0,1] that satisfies: (1) P(Ω) = 1, (2) P(A) ≥ 0 for all A ∈ F, and (3) For disjoint events A1, A2, ..., P(∪Ai) = ΣP(Ai).
50. How is the concept of a probability space represented in set notation?
A probability space is a triple (Ω, F, P), where Ω is the sample space, F is a σ-algebra on Ω, and P is a probability measure on F.
51. How is the concept of a random variable represented in set notation?
A random variable X is a function X: Ω → R, where Ω is the sample space and R is the set of real numbers. In set notation, we often write {X ∈ B} to represent the event that X takes a value in the set B.
52. How is the concept of a probability distribution function represented in set notation?
For a discrete random variable X, the probability distribution function is represented as P(X = x) for all possible values x. For a continuous random variable, it's represented as f(x), where P(a ≤ X ≤ b) = ∫[a,b] f(x)dx.
53. How is the concept of a joint probability distribution represented in set notation?
For discrete random variables X and Y, the joint probability distribution is represented as P(X = x, Y = y) for all possible combinations of x and y. For continuous random variables, it's represented as f(x,y), where P(X ∈ A, Y ∈ B) = ∫∫[A×B] f(x,y)dxdy.
54. How is the concept of stochastic independence represented in set notation?
Events A and B are stochastically independent if P(A ∩ B) = P(A) × P(B). For multiple events, A1, A2, ..., An are independent if P(∩Ai) = ΠP(Ai) for all subsets of the events.
55. What does P(A | B ∩ C) = P(A | B) imply about events A, B, and C?
If P(A | B ∩ C) = P(A | B), it implies that A is conditionally independent of C given B. The occurrence of C does not affect the probability of A, given that B has occurred.

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