Bayesian Inference is a statistical framework for updating probability estimates as new evidence is observed, based on Bayes’ theorem. It combines prior beliefs about model parameters with observed data to produce a posterior distribution that quantifies uncertainty over possible outcomes. In machine learning, Bayesian inference underpins probabilistic models, uncertainty estimation, and methods such as variational inference and Markov chain Monte Carlo. Applications include scientific modeling, decision making under uncertainty, and robust AI system design.
Machine learning is shifting from learning from data alone to learning from both data and teacher models. Beta-KD uses uncertainty-aware Bayesian weighting to train compact multimodal AI without blindly trusting every teacher signal.
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