PMF is an acronym that stands for Probability Mass Function. It is a term commonly used in the field of statistics and probability theory. A Probability Mass Function is a function that defines the probability of each possible outcome for a discrete random variable.
In simpler terms, when we have a discrete random variable, such as rolling a dice or flipping a coin, a PMF assigns a probability to each possible outcome. The probabilities must satisfy certain properties, such as being non-negative and summing up to 1.
The PMF is often represented as a table, graph, or formula and allows us to determine the likelihood of various outcomes. For example, if we have a fair six-sided die, the PMF would show that each face has an equal probability of 1/6.
PMF functions are valuable in various applications, such as analyzing data, making predictions, and understanding probability distributions. They help us describe and quantify the uncertainty associated with discrete random variables.
It is important to note that PMFs are applicable only to discrete random variables, where each possible outcome has a distinct probability. For continuous random variables, we use Probability Density Functions (PDFs).
