Bayesian Learning

Bayesian learning is a method of updating beliefs based on new evidence. It uses probability to represent uncertainty about a hypothesis. When new data arrives, Bayes' theorem calculates how likely the hypothesis is given that data, adjusting previous beliefs accordingly. This approach allows models to learn and improve over time by continuously refining predictions as more information becomes available, making it a powerful framework for decision-making under uncertainty across various fields like statistics, machine learning, and scientific research.