What is a Discrete Distribution?

In statistics, a discrete distribution is a probability distribution of the outcomes of finite variables or countable values. If a random variable follows the pattern of a discrete distribution, it means the random variable is discrete. In a broad sense, all probability distributions can be classified as either discrete probability distribution or continuous probability distribution. While a continuous distribution is premised on measurable values that are infinite, discrete distribution deals with the probabilities of the outcomes of finite values.

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How is a Discrete Distribution Used? 

There are several probability distributions, examples are uniform, Poisson, normal, binomial, chi-squared, geometric, exponential, gamma, and beta. All of these probability distributions as categorized either as a continuous distribution or discrete distribution. Through distribution, whether discrete or continuous, analysts and statisticians identify the outcomes of random variables. The probable outcomes are often presented on a chart using specific data points that explain the probability. Discrete distribution is both a statistical distribution and a mathematical analysis of outcomes of finite values. While a discrete distribution has a finite number of outcomes, continuous distribution has an infinite number of measurable outcomes.

Discrete Distribution Examples

Discrete distributions can be used in many real-life situations. Oftentimes, companies use this distribution in their inventory management to create a probability distribution that will help them adequately utilize their inventory. Poisson, binominal, multinominal and Bernoulli distribution are the most commonly used discrete distributions. Another instance of how discrete distribution can be used is in the Monte Carlo simulation that aids a prediction of outcomes and identify the underlying risks.