Bayesian Inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It combines prior knowledge (prior probability) with new data (likelihood) to form an updated probability (posterior probability). Bayesian inference is widely used in data science and machine learning for tasks such as parameter estimation, model selection, and predictive modeling. This approach allows for incorporating uncertainty and making probabilistic predictions based on evolving evidence. Bayesian methods are particularly useful in situations where data is sparse or where incorporating prior knowledge is beneficial.