Conditional Probability

Conditional Probability

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Vikram
Vikram Singh
Assistant Manager - Content
Updated on Dec 9, 2022 08:46 IST

Introduction

In this article, we will discuss one of the important concepts of statistics, Conditional Probability which is used in one of the machine learning algorithms known as the Naive Bayes Classifier.

2022_03_feature-images_CONDITIONAL_PROBABILITY.jpg

As the name suggests Conditional Probability, is the probability of an event under some given condition.

Confused! Don’t worry, we will discuss it briefly with some examples.

Table of Content:

Events

Any particular performance of a random experiment is called a trial and the combination of outcomes of these trials is termed as an event.

Example:

  • Rolling dice is a trial
    • Possible outcome {1, 2, 3, 4, 5, 6}
  • Possible events corresponding to rolling dice
    • Odd number of points: {1, 3, 5}
    • Even number of points: {2, 4, 6}
    • Points between 2 and 6: {3, 4, 5}

Union of Events:

If we have two events A and B, then Union of Events is defined as

Collection of all the outcome that are elements of either the set A or Set B or both of them.

It is denoted by A U B and represented by:

2022_03_conditional_probability_union.jpg

Example:

From the above example of dice: 

  • A- Odd number of points
    • {1, 3, 5}
  • B- Points between 2 and 6
    • {3, 4, 5}

Then, A U B = {1, 3, 4, 5}

Intersection of Events:

If we have two events A and B, then Intersection of Events is defined as:

Collection of all the outcome that are common in both the sets A and B.

2022_03_inter.jpg
2022_03_conditional_probability_intersection.jpg

Example:

From the above example of dice: 

  • A- Odd number of points
    • {1, 3, 5}
  • B- Points between 2 and 6
    • {3, 4, 5}

Then,

2022_03_a-intersection-b.jpg

Disjoint Events:

Events that don’t occur at the same time are known as disjoint events.

Or 

If we have two events A and B and they have no common element then both the events are termed as Disjoint events.

Example:

From the above example of dice:

  • A- Odd number of points
    • {1, 3, 5}
  • B- Even number of points 
    • {2, 4, 6}

Event A and B both have no common points, so A and B are disjoint.

2022_03_disjoint.jpg

Independent Events:

Two events A and B are said to be independent if the outcome of event A doesn’t depend on event B and vice versa.

Example: In throwing a coin, getting Head and Tails both of these events are independent.

2022_03_probability-of-independent-events.jpg

Mutually Exclusive Events:

Two events A and B are said to be mutually exclusive or disjoint if both of them can’t occur at the same time.

Example: Tossing a coin, the event Head and Tail are mutually exclusive events.

Probability of Mutually exclusive events: P (A U B) = P (A) + P (B)

2022_03_mutually-exclusive.jpg

Conditional Probability:

Condition Probability is the probability of an event occurring given that another event has already occurred.

i.e. we try to calculate the probability of the second event given that the first event has already occurred.

Example:

It will be raining at the end of the hottest day.

Here the probability of occurrence of rainfall depends on the temperature throughout the day.

Mathematical Formula:

2022_03_conditional-proabability-formula.jpg

Let’s understand the conditional probability with an example

Example:

Let there are 100 (47 boys, 53 girls) students in a classroom, the given table describes the number of students (boys and girls) who pass or fail in the exam.

Pass Fail Total
Boys 15 32 47
Girls 29 24 53
Total 44 56 100

Let A: represent passed in the exam

And B: represent student is a Boy

2022_03_conditional-proabability-example.jpg

Bayes’ Theorem:

Bayes’ theorem is an extension of Conditional Probability.

It includes two conditional probabilities.

It gives the relation between conditional probability and its reverse form.

2022_03_bayes-theorem-formula.jpg

Conclusion

In this article, we have briefly explained one of the important concepts of probability Conditional Probability which is widely used in machine learning algorithms.

Hope this article will help you in your data science and machine learning journey.

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About the Author
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Vikram Singh
Assistant Manager - Content

Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio

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