The Bayesian framework is a statistical approach to making decisions and drawing inferences by incorporating prior knowledge or beliefs, as well as new evidence or data. In the Bayesian framework, probabilities represent degrees of belief rather than frequencies, and these probabilities are updated as new information becomes available. Bayes' theorem is a fundamental principle in Bayesian statistics, which allows for the calculation of posterior probabilities based on prior probabilities and the likelihood of the data. Bayesian methods are widely used in various fields such as machine learning, data analysis, and decision-making, due to their flexibility and ability to handle uncertainty and variability in data.