Área de Bayes

This application developed by PET Statistics-UFSCar offers a geometric interpretation that facilitates the understanding of Bayes' theorem.
The application is interactive, that is, the user can provide the a priori probabilities they want, as well as the probabilities of the evidence ("data" or "sample") conditional on each hypothesis (likelihood) and each of them is represented by areas.
In addition, the posterior probability of each hypothesis is also calculated (probability of the hypothesis given the evidence); The application illustrates cases in which there are two or three possible hypotheses.
Using areas allows you to visualize how probabilities are updated with the incorporation of new information (the "evidence"), making the abstract concepts of conditional probabilities more tangible and understandable.
Bayes' theorem is fundamental to statistical inference: it allows the probabilities of each hypothesis to be updated as new information is acquired. This allows it to be widely applied in diverse areas, including medicine, economics, artificial intelligence and machine learning, where it is used for modeling uncertainty and making decisions based on empirical data.