Probabilistic inference is a branch of artificial intelligence and machine learning that deals with using probability theory to make predictions or decisions based on incomplete or uncertain information. It involves designing algorithms and models that can infer the probability distribution of unknown variables given observed data. Probabilistic inference is used in a wide range of applications, including natural language processing, computer vision, and robotics, where uncertainty and variability are inherent in the data. This field is concerned with reasoning under uncertainty and making decisions that are optimal given the available information and the uncertainty associated with it.