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Algebra of Events

Algebra of Events

Edited By Komal Miglani | Updated on Jul 02, 2025 07:52 PM IST

The algebra of events is the set of rules and regulations that shows how the event has occurred. 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.

Algebra of Events
Algebra of Events

Algebra of Events

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

When a die is thrown, sample space $S = \{1, 2, 3, 4, 5, 6\}.$

Let $A = \{2, 3, 5\},$ $B = \{1, 3, 5\},$ $C = \{2, 4, 6\} $

Here, $A$ is the event of the occurrence of prime numbers, $B$ is the event of the occurrence of odd numbers and $C$ is the event of the occurrence of even numbers.

Also, observe that $A, B,$ and $C$ are subsets of $S$.

Now, What is the Occurrence of an event?

From the above example, the experiment of throwing a die. Let $E$ denote the event “ a number less than $4$ appears”. If any of $' 1’$ or $ '2'$ or $'3'$ had appeared on the die then we say that event $E$ has occurred.

Thus, the event $E$ of a sample space $S$ is said to have occurred if the outcome $ω$ of the experiment is such that $ω ∈ E$. If the outcome $ω$ is such that $ω ∉ E$, we say that the event $E$ has not occurred.

Complimentary Event

For every event $A$, there corresponds to another event $A$' which contains all outcomes in sample space that are not covered in $A$. Such event is called the complementary event to $A$. It is also called the event ‘not $A$’.

For example, take the experiment ‘of tossing two coins’. The sample space is

$S = \{HH, HT, TH, TT\}$

Let $A=\{HT, TH\}$ be the event ‘only one tail appears’

Thus the complementary event ‘not $ A$’ to the event $A$ is

$A’ = \{HH, TT\}$

or $ A’ = \{ω : ω ∈ S$ and $ω ∉A\} = S - A$

The Event $'A$ or $B’$

As we have studied in the first chapter, ‘Sets’, the union of two sets $A$ and $B$ denoted by $A ∪ B$ contains all those elements which are either in $A$ or in $B$ or in both.

When the sets $A$ and $B$ are two events associated with a sample space, then $A ∪ B$ is the event ‘either $A$ or $B$ or both’. This event $A ∪ B$ is also called $'A$ or $B’.$

Therefore

Event
$
\begin{aligned}
\mathbf{A} \text { or } \mathbf{B} & =\mathbf{A} \cup \mathbf{B} \\
& =\{\omega: \omega \in \mathbf{A} \text { or } \omega \in \mathbf{B}\}
\end{aligned}
$

The Event $'A$ and $B’$

The intersection of two sets $A ∩ B$ is the set of those elements which are common to both $A$ and $B$. i.e., which belong to both $'A$ and $B’.$

If $A$ and $B$ are two events, then the set $A ∩ B$ denotes the event $'A$ and $B’.$

The Event $'A$ but not $B’$

The $A – B$ is the set of all those elements which are in $A$ but not in $B$. Therefore, the set $A – B$ may denote the event $'A$ but not $B’.$

Also, $A - B = A ∩ B´$ or $A - ( A ∩ B).$

Equally Likely Events

Equally likely means that each outcome of an experiment occurs with equal probability. For example, if you toss a fair, six-sided die, each face $(1, 2, 3, 4, 5, or 6)$ is as likely to occur as any other face. If you toss a fair coin, a Head $(H)$ and a Tail $(T)$ are equally likely to occur. If you randomly guess the answer to a true/false question on an exam, you are equally likely to select a correct answer or an incorrect answer.

Exhaustive events

Consider the experiment of rolling a die. The associated sample space is

$S = \{1, 2, 3, 4, 5, 6\}$

Let us define the following events

$ A:$ ‘a prime number number less than $6$ appears’,

$B:$ ‘a number less than $2$ appears’

and $C:$ ‘a number greater than $3$ appears’.

Then $A = \{2, 3, 5\}, B = \{1\}$ and $C = \{4, 5, 6\}.$

Observe that, $A ∪ B ∪ C = \{2, 3, 5\} ∪ \{1\} ∪ \{4, 5, 6\} = \{1, 2, 3, 4, 5, 6\} = S$

So if union of given events equals sample space, then these events are called a system of exhaustive events. Thus events A, B and C are called exhaustive events in this case.

In general, $E_1, E_2, \ldots, E_n$ are $n$ events of a sample space $S$ and if

$\mathrm{E}_1 \cup \mathrm{E}_2 \cup \mathrm{E}_3 \cup \ldots \cup \mathrm{E}_n=\bigcup_{i=1}^n \mathrm{E}_i=\mathrm{S}$

then $E_1, E_2, \ldots, E_n$ are called exhaustive events.

Mutually exclusive events

Consider the experiment of rolling a die. The associated sample space is

$ S = \{1, 2, 3, 4, 5, 6\}$

Consider events, $A$ ‘an odd number appears’ and $B$ ‘an even number appears’

$A = \{1, 3, 5\}$ and $B = \{2, 4, 6\}$

Clearly $A ∩ B = φ$, i.e., $A$ and $B$ are disjoint sets.

In general, two events $A$ and $B$ are called mutually exclusive events if the occurrence of any one of them excludes the occurrence of the other event, i.e., if they can not occur simultaneously. In this case the sets $A$ and $B$ are disjoint.

Recommended Video Based on Algebra of Events


Solved Examples Based on Algebra of Events:

Example 1: Which of the following events are exhaustive events in the case of a deck of $52$ cards?

1) $P$ = Drawing a King

$Q$ = Drawing a Black card.

2) $P$ = Drawing a numbered card

$Q$ = Drawing a Black card.

3) $P$ = Drawing a numbered card

$Q$ = Drawing a Red card

$R$ = Drawing a Face card.

4) $P$ = Drawing Black cards

$Q$ = Drawing face cards.

Solution

Since numbered cards are $\{A, 2, \cdots \ldots \ldots \ldots, 10\}$
Red cards are $\{R 1, R 2, \cdots \cdots \cdots \cdots \cdots, R J, R Q, R K, R A\}$and face cards are $\{J, Q, K\}$.

The union of these three events gives us the sample space.

So, these three are exhaustive events.

Hence, the answer is the option (3).

Example 2: Which of the following types of numbers are mutually exclusive?

1) $\mathbb{R}, \mathbb{Z}$
2) $R^{+}, R$
3) $\mathbb{Z}, \mathbb{N}$
4) $W, R^{-}$

Solution

Here $R$= Real Number

$R^ +$= Positive real Number

$\mathbb{Z}=$ Integers, $W=$ Whole Number, $N=$ Natural Number,
Since $W>0$ and $R^{-}<0$

So $W$ and $\mathrm{R}^{-}$do not have any common element. So these are mutually exclusive events.

Hence, the answer is the option (4).

Example 3: A die is thrown. Let $A$ be the event that the number obtained is greater than $3$. Let $B$ be the event that the number obtained is less than $5$. Then $P(A \cup B)$ is
1) $\frac{2}{5}$
2) $\frac{3}{5}$
3) $0$
4) $1$

Solution

Algebra of events

$A \cup B \rightarrow$ at least one event.
$A \cap B \rightarrow$ both occur simultaneously.
$A \cap \bar{B}=A-B$, the occurrence of event A but not B .
$\bar{A} \cap \bar{B} \rightarrow$ neither A or B.]

where $A$ & $B$ are any two events.

Set $A=\{4,5,6\}$
$\operatorname{Set} B=\{1,2,3,4\}$
Set $A \cup B=\{1,2,3,4,5,6\}$
Which is a set of all possible outcomes. Hence

$P(A \cup B)=1$

Hence, the answer is the option 4.

Example 4: Let $A$ and $B$ be two events such that $P(\overline{A \cup B})=\frac{1}{6}, \quad P(A \cap B)=\frac{1}{4}$ and $P(\bar{A})=\frac{1}{4}$, where $\bar{A}$ stands for the complement of event $A$. Then events $A$ and $B$ are

1) equally likely but not independent

2) equally likely and mutually exclusive

3) mutually exclusive and independent

4) independent but not equally likely

Solution

We have,

$
\begin{aligned}
& P(A \cup B)=1-P(\overline{A \cup B})=\frac{5}{6} \\
& P(A \cap B)=\frac{1}{4} \\
& P(A)=1-P(\bar{A})=\frac{3}{4}
\end{aligned}
$

Also,

$
\begin{aligned}
& P(A \cup B)=P(A)+P(B)-P(A \cap B) \\
& \frac{5}{6}=\frac{3}{4}+P(B)-\frac{1}{4} \\
& P(B)=\frac{1}{3}
\end{aligned}
$

Now,

$
P(A \cap B)=\frac{1}{4} \text { and } P(A) \cdot P(B)=\frac{3}{4} \times \frac{1}{3}=\frac{1}{4}
$

so, $P(A) \cdot P(B)=P(A \cap B)$
Therefore, Events A \& B are independent
But as $P(A) \neq P(B)$, so they are not equally likely.
Also as $P(A \cap B)$ does not equal $0$ , so they are not mutually exclusive

Hence, the answer is the option 4.

Example 5: Two aeroplanes I and II bomb a target in succession. The probabilities of I and II scoring a hit correctly are $0.3$ and $0.2,$ respectively. The second plane will bomb only if the first misses the target. The probability that the target is hit by the second plane is (assume that different planes hitting the target are independent events)

1) $0.2$

2) $0.7$

3) $0.06$

4) $0.14$

Solution

Let $ A$ be the event that the first plane hits the target, and

$B$ be the event that the second plane hits the target

So we need $P(A' ∩ B)$

As $A$ and $B$ will hit the target independently, A and B are independent events, and thus $A'$ and $B$ are also independent events

So $P(A' ∩ B) = P(A').P(B) = (1 - P(A)).(P(B)) = (0.7)*(0.2) = 0.14$

Hence, the answer is the option 4.

Summary
The algebra of events provides a framework for analyzing the relationship between different events in probability. These methods are widely used in real-life applications providing insights and solutions to complex problems. Mastery of these concepts can help in solving gaining deeper insights and contributing meaningfully to real-life problems.

Frequently Asked Questions (FAQs)

1. What is a event?

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

2. What are 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.

3. What are complimentary events?

For every event $A$, there corresponds to another event $A$' which contains all outcomes in sample space that are not covered in $A$. Such event is called the complementary event to $A$.

4. What are equally likely events?

Equally likely events means that each outcome of an experiment occurs with equal probability.

5. What are exhaustive events?

If union of given events equals sample space, then these events are called a system of exhaustive events.

6. What is the algebra of events in probability?
The algebra of events in probability refers to the set of rules and operations used to manipulate and combine events. It allows us to work with complex events by breaking them down into simpler components or combining simpler events to form more complex ones. This framework is essential for calculating probabilities of compound events.
7. How does the algebra of events relate to set theory?
The algebra of events is closely related to set theory. Events in probability can be thought of as sets of outcomes from a sample space. The operations used in the algebra of events (union, intersection, complement) correspond directly to set operations. This connection allows us to apply set theory principles to solve probability problems.
8. What is the difference between mutually exclusive events and independent events?
Mutually exclusive events are events that cannot occur simultaneously. If one happens, the other cannot. For example, rolling an even number and an odd number on a single die roll are mutually exclusive. Independent events, on the other hand, are events where the occurrence of one does not affect the probability of the other. For instance, flipping a coin and rolling a die are independent events.
9. How do you calculate the probability of the union of two events?
The probability of the union of two events A and B is calculated using the addition rule: P(A ∪ B) = P(A) + P(B) - P(A ∩ B). This formula accounts for the possibility of double-counting outcomes that are in both events. If the events are mutually exclusive, the last term becomes zero, simplifying the formula to P(A ∪ B) = P(A) + P(B).
10. What is the complement of an event, and how is its probability calculated?
The complement of an event A, denoted as A' or A^c, is the event that A does not occur. It includes all outcomes in the sample space that are not in A. The probability of the complement is calculated as P(A') = 1 - P(A). This relationship is fundamental in probability theory and is often used to solve problems indirectly.
11. How does the distributive property apply to the algebra of events?
The distributive property in the algebra of events states that A ∩ (B ∪ C) = (A ∩ B) ∪ (A ∩ C) and A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C). This property allows us to expand or simplify complex event expressions, much like in algebra. It's particularly useful when dealing with compound events involving multiple conditions.
12. What is De Morgan's Law in the context of probability?
De Morgan's Laws in probability are: (A ∪ B)' = A' ∩ B' and (A ∩ B)' = A' ∪ B'. These laws state that the complement of a union is the intersection of the complements, and the complement of an intersection is the union of the complements. These laws are powerful tools for simplifying complex probability expressions and solving problems involving multiple events.
13. How do you interpret the intersection of events in real-world scenarios?
The intersection of events, denoted as A ∩ B, represents the occurrence of both events A and B simultaneously. In real-world scenarios, this often translates to satisfying multiple conditions at once. For example, in a survey, the intersection might represent people who both own a car AND have a college degree. Understanding intersections is crucial for analyzing situations with multiple criteria or characteristics.
14. What is the empty set in probability, and why is it important?
The empty set, denoted as ∅, represents an impossible event in probability – an event that can never occur. Its probability is always 0. The empty set is important because it serves as a boundary case in probability calculations and helps define mutually exclusive events. Understanding the empty set is crucial for correctly interpreting and solving probability problems, especially those involving complex event combinations.
15. How does the concept of subsets apply to probability?
In probability, if event A is a subset of event B (A ⊆ B), it means that every outcome in A is also in B. This implies that the probability of A is less than or equal to the probability of B: P(A) ≤ P(B). Understanding subsets helps in analyzing hierarchical or nested events and is crucial for correctly applying probability rules, especially when dealing with conditional probabilities.
16. What is the significance of the universal set in probability?
The universal set, often denoted as Ω or S, represents the entire sample space – all possible outcomes of an experiment. It's significant because every event is a subset of the universal set, and the probability of the universal set is always 1. Understanding the universal set helps in defining events, calculating complements, and ensuring that all possible outcomes are considered in probability calculations.
17. How do you handle the union of more than two events?
The union of more than two events can be handled using the Inclusion-Exclusion Principle. For three events A, B, and C, the formula is:
18. What is a partition of a sample space, and why is it useful?
A partition of a sample space is a set of mutually exclusive and exhaustive events that divide the entire sample space. It's useful because the sum of probabilities of all events in a partition equals 1, and any event in the sample space can be expressed as a union of some events in the partition. Partitions are particularly helpful in applying the law of total probability and in breaking down complex problems into simpler, manageable parts.
19. How does the concept of disjoint events relate to mutually exclusive events?
Disjoint events and mutually exclusive events are the same concept. Both terms refer to events that cannot occur simultaneously – the occurrence of one precludes the occurrence of the other. Mathematically, this means their intersection is the empty set: A ∩ B = ∅. Understanding disjoint events is crucial for correctly applying probability addition rules and avoiding common mistakes in probability calculations.
20. What is the difference between P(A|B) and P(A ∩ B)?
P(A|B) represents the conditional probability of A given that B has occurred, while P(A ∩ B) is the probability of both A and B occurring. They are related by the formula: P(A|B) = P(A ∩ B) / P(B). The key difference is that P(A|B) considers B as given information, adjusting the probability space, while P(A ∩ B) considers the likelihood of both events happening in the original probability space.
21. How does the commutative property apply to the algebra of events?
The commutative property in the algebra of events states that A ∪ B = B ∪ A and A ∩ B = B ∩ A. This means the order of events doesn't matter when taking unions or intersections. This property is fundamental in simplifying probability expressions and in understanding that the sequence of considering events doesn't affect the final outcome in these operations.
22. What is the associative property in the algebra of events?
The associative property in the algebra of events states that (A ∪ B) ∪ C = A ∪ (B ∪ C) and (A ∩ B) ∩ C = A ∩ (B ∩ C). This property allows us to group events in different ways without changing the result. It's particularly useful when dealing with multiple events, as it provides flexibility in how we approach complex probability calculations.
23. How do you interpret P(A ∪ B) - P(A ∩ B)?
P(A ∪ B) - P(A ∩ B) represents the probability of either A or B occurring, but not both simultaneously. This is sometimes called the "symmetric difference" of A and B. It's a useful concept in understanding how events overlap and in solving problems where we're interested in exclusive occurrences of events.
24. What is the principle of duality in probability?
The principle of duality in probability states that for any true statement about unions and intersections, the dual statement obtained by interchanging unions with intersections and complements with the universal set is also true. For example, if A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C) is true, then A ∩ (B ∪ C) = (A ∩ B) ∪ (A ∩ C) is also true. This principle helps in deriving new formulas and understanding the symmetry in probability laws.
25. How does the concept of a sigma-algebra relate to the algebra of events?
A sigma-algebra is a collection of subsets of the sample space that satisfies certain properties: it contains the empty set, is closed under complementation, and is closed under countable unions. The algebra of events is a specific case of a sigma-algebra. Understanding sigma-algebras is crucial for more advanced probability theory, as it provides a rigorous foundation for defining probability measures on sets of events.
26. What is the difference between finite and infinite sample spaces in probability?
Finite sample spaces contain a countable number of outcomes, while infinite sample spaces have an uncountable number of outcomes. This distinction affects how probabilities are calculated and interpreted. In finite spaces, probabilities can often be calculated by counting favorable outcomes. In infinite spaces, more advanced techniques like integration or limits may be needed. Understanding this difference is crucial for correctly applying probability concepts in various scenarios.
27. How does the concept of a power set relate to the algebra of events?
The power set of a sample space is the set of all possible subsets of that space, including the empty set and the space itself. In the algebra of events, each event is a subset of the sample space, so the set of all possible events is the power set. Understanding the power set helps in comprehending the full range of possible events and their combinations, which is crucial for complex probability problems and theoretical foundations.
28. What is the significance of the axioms of probability in the algebra of events?
The axioms of probability provide the fundamental rules that govern probability calculations within the algebra of events. These axioms state that probabilities are non-negative, the probability of the entire sample space is 1, and the probability of a union of mutually exclusive events is the sum of their individual probabilities. These axioms ensure consistency and logical coherence in probability theory and form the basis for all probability calculations and theorems.
29. How do you interpret P(A) + P(B) when A and B are not mutually exclusive?
When A and B are not mutually exclusive, P(A) + P(B) overestimates the probability of either event occurring because it double-counts the intersection. The correct probability of either A or B occurring is P(A ∪ B) = P(A) + P(B) - P(A ∩ B). Understanding this is crucial for avoiding common errors in probability calculations, especially when dealing with overlapping events.
30. What is the relationship between conditional probability and the intersection of events?
Conditional probability P(A|B) is related to the intersection of events through the formula: P(A|B) = P(A ∩ B) / P(B). This relationship shows that the conditional probability of A given B is the proportion of B's probability that is also in A. Understanding this connection is fundamental for solving problems involving dependent events and for applying Bayes' theorem.
31. How does the concept of independence affect the algebra of events?
Independence of events A and B means that P(A ∩ B) = P(A) × P(B). This property simplifies many calculations in the algebra of events. For independent events, the occurrence of one does not affect the probability of the other. This concept is crucial for correctly analyzing situations where events do not influence each other and for simplifying complex probability expressions.
32. What is the law of total probability, and how does it use the algebra of events?
The law of total probability states that for a partition {B₁, B₂, ..., Bₙ} of the sample space, the probability of an event A is:
33. How do you interpret P(A') × P(B')?
P(A') × P(B') represents the probability that neither event A nor event B occurs, assuming A and B are independent. It's the product of the probabilities of the complements of A and B. This interpretation is useful in scenarios where we're interested in the likelihood of multiple events not occurring, such as in reliability analysis or in calculating the probability of avoiding multiple risks.
34. What is the difference between P(A ∪ B) and P(A) ∪ P(B)?
P(A ∪ B) is a valid probability representing the likelihood of either event A or event B (or both) occurring. On the other hand, P(A) ∪ P(B) is not a meaningful expression in probability theory. Probabilities are numbers, not sets, so the union operation (∪) cannot be applied to them. This distinction highlights the importance of understanding that probability operations apply to events, not to the probability values themselves.
35. How does the concept of a null event relate to the empty set in probability?
A null event in probability is an event with a probability of zero, which is equivalent to the empty set in set theory. However, it's important to note that while all empty sets are null events, not all null events are necessarily empty sets in infinite sample spaces. Understanding null events is crucial for correctly interpreting probability results, especially in continuous probability distributions where specific points can have zero probability despite being possible outcomes.
36. What is the significance of the idempotent property in the algebra of events?
The idempotent property in the algebra of events states that A ∪ A = A and A ∩ A = A. This property means that repeating an event in a union or intersection doesn't change the result. It's significant because it simplifies calculations and helps in understanding the nature of event combinations. This property is particularly useful when simplifying complex event expressions or when dealing with repeated events in probability scenarios.
37. How does the concept of a complement distribute over union and intersection?
The distribution of complements over union and intersection is described by De Morgan's Laws: (A ∪ B)' = A' ∩ B' and (A ∩ B)' = A' ∪ B'. These laws show how the complement of a compound event can be expressed in terms of the complements of its component events. Understanding this distribution is crucial for simplifying complex probability expressions and for solving problems involving multiple events and their complements.
38. What is the difference between P(A|B) and P(B|A), and why is it important?
P(A|B) is the probability of A occurring given that B has occurred, while P(B|A) is the probability of B occurring given that A has occurred. These are generally not equal unless A and B are independent or have a special relationship. Understanding this difference is crucial for correctly applying conditional probability, avoiding the common mistake of confusing the direction of conditioning, and for applying Bayes' theorem in probability problems.
39. How does the concept of a sample space partition relate to conditional probability?
A sample space partition divides the sample space into mutually exclusive and exhaustive events. In conditional probability, partitions are useful because they allow us to apply the law of total probability: P(A) = Σ P(A|Bᵢ)P(Bᵢ), where {Bᵢ} is a partition. This relationship is fundamental in solving complex probability problems by breaking them down into simpler conditional probabilities over a partition of the sample space.
40. What is the significance of the absorption law in the algebra of events?
The absorption law in the algebra of events states that A ∪ (A ∩ B) = A and A ∩ (A ∪ B) = A. This law is significant because it allows simplification of complex event expressions. It shows that adding an intersection (or union) of an event with itself and another event doesn't change the original event. This principle is useful in simplifying probability calculations and in understanding the relationships between compound events.
41. How does the concept of pairwise disjoint events differ from mutually exclusive events?
Pairwise disjoint events and mutually exclusive events are

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