Bayesian Learning

Bayesian learning represents uncertainty using probability. A prior probability is updated with evidence to obtain a posterior probability.

Japanese terms

1. Bayesian learning — ベイズ学習がくしゅう: Learning that applies Bayes’ theorem to update uncertainty about models or parameters.
2. Prior probability — 事前確率じぜんかくりつ: A probability expressing belief before incorporating the current evidence.
3. Posterior probability — 事後確率じごかくりつ: A probability updated after combining prior belief with observed evidence.

Related topics

Probabilistic models provide the wider modeling framework in which Bayesian methods operate.