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Probability can be applied for more than computing the likelihood of one event; it can summarize the likelihood of all possible outcomes. A thing of interest in probability is named a random variable, and the nexus between each possible outcome for a random variable and their probabilities is called a probability distribution

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• PMF(Probability Mass Function), Discrete random variable, and CDF

  
  

A discrete probability distribution represents the probabilities for a discrete random variable, in which the set of data can only be comprised of a limited number of unique outcomes.

Binary and Categorical

The Discrete Cumulative Distribution Function calculates the sum of total outcomes of the previous probabilities.

• PDF(Probability Density Function), Continuous Distribution

  
  

The continuous probability distribution summarizes the probabilities for a continuous random variable.

The relationship between Probability density function and Cumulative probability function is CDF computation equivalents to the integral in calculus, whereas PDF represents the derivative of the current data.