Mean of Probability Density Function
The mean of the probability density function refers to the average value of the random variable. The mean is also called as expected value or expectation. It is denoted by μ or E[X] where, X is random variable.
The mean of the probability density function f(x) for the continuous random variable X is given by:
[Tex]\bold{E[X] = \mu = \int\limits^{\infin}_{-\infin}xf(x)dx} [/Tex]
Probability Density Function
Probability Density Function is the function of probability defined for various distributions of variables and is the less common topic in the study of probability throughout the academic journey of students. However, this function is very useful in many areas of real life such as predicting rainfall, financial modelling such as the stock market, income disparity in social sciences, etc.
This article explores the topic of the Probability Density Function in detail including its definition, condition for existence of this function, as well as various examples.
Table of Content
- What is Probability Density Function?
- Probability Density Function Example
- Probability Density Function Formula
- How to Find Probability from Probability Density Function
- Graph for Probability Density Function
- Probability Density Function Properties
- Mean of Probability Density Function
- Median of Probability Density Function
- Variance Probability Density Function
- Standard Deviation of Probability Density Function
- Probability Density Function Vs Cumulative Distribution Function
- Types of Probability Density Function
- Probability Density Function for Uniform Distribution
- Probability Density Function for Binomial Distribution
- Joint Probability Density Function
- Applications of Probability Density Function
- Solved Examples on Probability Density Function