Probability model

Probability model is a mathematical framework that uses probability distributions to represent and quantify uncertainty in data, events, or system behaviors. In artificial intelligence and machine learning, probability models describe the likelihood of different outcomes occurring given specific inputs or conditions. These models assign numerical probabilities to possible events, enabling systems to make informed decisions under uncertainty. Common types include Bayesian networks, Markov models, and Gaussian mixture models.

Probability models form the foundation for statistical inference, predictive analytics, and risk assessment in AI applications. They enable machines to estimate confidence levels, handle missing data, and incorporate prior knowledge into decision-making processes. Unlike deterministic models that produce fixed outputs, probability models generate probability distributions that capture the range of possible outcomes and their associated likelihoods, making them essential for robust AI system design.