Discrete distributions are a fundamental concept in probability theory and statistics, representing the probabilities of outcomes in scenarios where the possible results are countable and distinct. Unlike continuous distributions, which deal with outcomes that can take on any value within a range, discrete distributions focus on finite or countably infinite sets of outcomes, such as the roll of a die or the number of successes in a series of trials. Common examples include the Bernoulli, binomial, and Poisson distributions, each serving unique applications in data analysis and decision-making processes across various fields, including data science and artificial intelligence.