Bayesian probability is a statistical approach that updates the likelihood of an event based on new evidence or data. It is powered by Bayes’ Theorem, making it widely used in machine learning, artificial intelligence, forecasting, and data-driven decision-making.
How Bayesian Probability Works
- Begin with an initial assumption (prior probability)
- Receive new information or evidence
- Update the assumption using Bayes’ Theorem → new probability (posterior probability)