# Difference Between Pdf And Pmf In Probability Distribution

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Continuing in the context of Example 3.

A probability distribution is a way to represent the possible values and the respective probabilities of a random variable. There are two types of probability distributions: discrete and continuous probability distribution. As you might have guessed, a discrete probability distribution is used when we have a discrete random variable.

A probability mass function differs from a probability density function pdf in that the latter is associated with continuous rather than discrete random variables; the values of the latter are not probabilities as such: a pdf must be integrated over an interval to yield a probability. PDF, on the other hand, is used when you need to come up with a range of continuous random variables. PDF uses continuous random variables. CDF is used to determine the probability wherein a continuous random variable would occur within any measurable subset of a certain range. Source: 1. Wikipedia 2.

## Probability Distributions and their Mass/Density Functions

Sign in. However, for some PDFs e. Even if the PDF f x takes on values greater than 1, i f the domain that it integrates over is less than 1 , it can add up to only 1. As you can see, even if a PDF is greater than 1 , because it integrates over the domain that is less than 1 , it can add up to 1. Because f x can be greater than 1.

PDF (Probability Density Function) is the likelihood of the random variable in the range of discrete value. On the other hand, PMF (Probability Mass Function) is.

## Differences Between PDF and PMF

A random variable is a variable whose value is not known to the task; in other words, the value depends on the result of the experiment. For instance, while flipping a coin, the value i. PDF Probability Density Function is the likelihood of the random variable in the range of discrete value. On the other hand, PMF Probability Mass Function is the likelihood of the random variable in the range of continuous values.

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