Bayesian reasoning is a statistical method that updates the probability of a hypothesis as more evidence becomes available. It is based on Bayes' theorem, which combines prior knowledge with new data to refine predictions. This approach allows for a more flexible understanding of uncertainty and helps in making informed decisions. In Bayesian reasoning, the initial belief, known as the prior, is adjusted using the likelihood of observing the new evidence, resulting in a revised belief called the posterior. This iterative process is widely used in various fields, including machine learning, medicine, and finance, to improve decision-making under uncertainty.