Bayesfelfogás

Bayesfelfogás, often translated as Bayesian interpretation or Bayesianism, is a philosophical approach to probability and reasoning. It views probability not as a measure of the frequency of an event occurring in the long run, but rather as a degree of belief or confidence in a proposition. This belief is quantifiable and can be updated as new evidence becomes available.

Central to Bayesfelfogás is Bayes' Theorem, a mathematical formula that describes how to revise existing beliefs in light of new evidence. The theorem allows for the calculation of a posterior probability (the updated belief) based on a prior probability (the initial belief) and the likelihood of observing the new evidence given the hypothesis. This iterative process of updating beliefs is a core feature of Bayesian reasoning.

Proponents of Bayesfelfogás argue that it provides a more coherent and rational framework for dealing with uncertainty than other interpretations of probability, such as frequentism. They contend that it is applicable to a wide range of problems, from scientific inference and machine learning to everyday decision-making. The subjective nature of prior beliefs is a point of discussion, but Bayesians emphasize that these priors should be reasonable and that the process of updating with evidence leads to convergence on more objective conclusions over time. The interpretation is widely used in fields like statistics, artificial intelligence, and physics.