Bayesiy
Bayesianism, also known as Bayesian inference, is a statistical approach to inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. The theorem, named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. It is a fundamental concept in probability theory and statistics.
Bayesian inference is characterized by its use of prior distributions to represent initial beliefs about unknown
One of the key advantages of Bayesian inference is its ability to provide a full probability distribution
However, Bayesian inference also has some limitations. It requires the specification of prior distributions, which can