Probabilistic inference is a method used to draw conclusions about uncertain events based on known information. It involves using probability theory to update beliefs when new evidence is available. This approach is widely applied in fields like statistics, machine learning, and artificial intelligence to make predictions or decisions under uncertainty. In probabilistic inference, models represent the relationships between variables, allowing for the calculation of the likelihood of different outcomes. Techniques such as Bayesian inference help in refining these models by incorporating prior knowledge and adjusting beliefs as new data emerges, leading to more accurate conclusions.