Understanding to PDF and CDF

What are PDF and CDF?

CDF is the cumulative density function which is used for continuous types of variables.
PDF is the probability density function for both discrete & continuous variables, each probability is between 0&1, all probability is the sum to 1.

  1. PDF ( Probability Density Function)
    This basically is a probability law for a continuous random variable say X ( for discrete, it is probability mass function).
    The probability law defines the chances of the random variable taking a particular value say x, i.e. P (X=x).
    However this definition is not valid for continuous random variables because the probability at a given point is zero.
    An alternate to this is: pdf= P (x-e<X<=x)/e as e tends to zero.

  2. CDF ( Cumulative Distribution Function)
    As the name cumulative suggests, this is simply the probability upto a particular value of the random variable, say x. Generally denoted by F, F= P (X<=x) for any value of x in the X space. It is defined for both discrete and continuous random variables.