## Introduction

A mathematical function that provides the probabilities of occurrence of various possible outcomes in an experiment is Probability Distribution. It is the possibility/ probability of every possible outcome of an experiment. Letâ€™s understand Probability Distribution with an example, let **x **be a random variable and denote the outcome of a roll of a dice, then the probability distribution of x can be denoted as-

It can be observed that the sum of all P(X) = 1.

## Types of Probability Distribution

Probability distribution is of two types: Discrete and Continuous Probability Distribution.

- If the probabilities of the random variable take only a discrete set of values, then the distribution is called a
**discrete probability distribution**. For example, the probability distribution of a random variable denoting the outcome of a roll of dice. - If the probabilities of the random variable take any value between two numbers i.e. a range of values then the distribution is called a
**continuous probability distribution**. For example, the probability distribution of a random variable denoting the temperature throughout the day.

### Cumulative Distribution Function

It is the probability that a random variable X will take a value less than or equal to x. For a discrete random variable,

F(x) = P(X<=x) = P(a)

For a continuous random variable,

F(x) = P(X<=x) = f(x)dx