##
__What is Probability Density Function (PDF)?__

The derivative of Cumulative Distribution Function (CDF) w.r.t. some dummy variable is called as probability density function (PDF).__What is Probability Density Function (PDF)?__

Probability density function can be defined mathematically as-

Relation between PDF and CDF (Formula of PDF) |

Now we will discuss the properties of probability density function. The derivation of properties of PDF is also provided here.

**ðŸŒ“**

__READ THIS ALSO__:-**Cumulative Distribution Function (CDF) - Properties of CDF - CDF Definition, Basics - Continuous and Discrete CDF**

##
__Watch the Complete Video Here-__

__Watch the Complete Video Here-__

##
__Properties of Probability Density Function (PDF) with Derivation__

**- Probability density function is always non-zero for all values of x.**

__Property 1__PDF Property 1 with Proof |

**Property 2**- The area under the PDF curve is always equal to Unity i.e. one.

PDF Property 2 With Proof |

**ðŸŒ“**

__READ THIS ALSO__:-**Joint Probability Density Function (Joint PDF) - Properties of Joint PDF with Derivation- Relation Between Probability and Joint PDF**

**- It is possible to get Cumulative Distribution Function (CDF) by integrating PDF.**

__Property 3__PDF Property 3 With Proof |

**- Probability of the event {x1< X<= x2} is given by the area under the PDF curve in**

__Property 4__{x1< X<= x2} range.

PDF Property 4 With Proof |

**Go to HOME Page**

__Read More-__**Random Variables (Discrete and Continuous Random Variables), Sample space and Random Variables Examples**

**Probability Density Function (PDF) - Definition, Basics and Properties of Probability Density Function (PDF) with Derivation and Proof**

## No comments:

## Post a Comment