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## Probability Density Function (PDF)

This is an expression used in statistics to define the likelihood of an outcome occurring (probability distribution. PDF is perfectly suited for discrete random variables and not continuous random variables. As you probably know, with a discrete random variable, you can estimate the precise variable value. If you use a graph to portray the probability density function, the variable will fall in the interval indicated by the area under the curve.

## Conditional Probability Mass Function (PMF)

The PMF of a discrete random variable can be used to attribute its probability function. This is usually done when some information that leads to conditional probability distribution needs to be considered. The joint PMF of a discrete variable should be known if we want to compute its conditional probability mass function.

## Joint Probability and Joint Probability Distribution

This is the likelihood of two phenomena occurring at the same time. In probability terminology, we can designate these two events as A and B and write them in this format: P(A n B) or P(A and B). Joint probability highlights the intersection of two or multiple events. A Venn diagram can always be used to showcase this intersection. On the other hand, a joint probability distribution describes the likelihood distribution for two or multiple random variables.

## Power series distribution

These are discrete distributions derived from power series on a subset of N. Power series distributions are essential because most discrete distributions are power series distributions.