Approximate inference is a method used in statistics and machine learning to estimate the properties of complex models when exact calculations are impractical. It helps in making predictions or understanding data by simplifying the problem, often using techniques like Monte Carlo methods or variational inference to generate estimates. This approach is particularly useful in scenarios involving large datasets or intricate models, such as Bayesian networks or deep learning. By providing a way to approximate solutions, it allows researchers and practitioners to draw meaningful conclusions without needing exhaustive computations.