bayésiennes

Bayésiennes refers to a school of thought in statistics that emphasizes the use of Bayes' theorem for updating beliefs in light of new evidence. This approach treats statistical parameters as random variables that have probability distributions, reflecting a degree of uncertainty. The core of Bayesian inference lies in combining prior beliefs (expressed as a prior probability distribution) with observed data (through a likelihood function) to produce a posterior probability distribution. This posterior distribution represents the updated beliefs about the parameters after considering the data.

The foundational principle is Bayes' theorem, which states that the posterior probability is proportional to the prior probability multiplied by the likelihood. Prior distributions can be chosen to be "uninformative" if there is little prior knowledge, or "informative" if existing knowledge is available. Bayesian methods are widely applied in various fields, including machine learning, economics, and scientific research, for tasks such as parameter estimation, hypothesis testing, and model comparison. The interpretation of results in a Bayesian framework is often more intuitive, as it directly provides probabilities of hypotheses or parameter values.