Posterior distribution

Definition: A way to summarize one’s updated knowledge in Bayesian inference, balancing prior knowledge with observed data. In statistical terms, posterior distributions are proportional to the product of the likelihood function and the prior. A posterior probability distribution captures (un)certainty about a given parameter value.

Related terms: <a href='/glossary/bayes-factor/'>Bayes Factor</a>, <a href='/glossary/bayesian-inference/'>Bayesian inference</a>, <a href='/glossary/bayesian-parameter-estimation/'>Bayesian parameter estimation</a>, <a href='/glossary/likelihood-function/'>Likelihood function</a>, <a href='/glossary/prior-distribution/'>Prior distribution</a>

References: Dienes (2014), Lüdtke et al. (2020), & van de Schoot et al. (2021)

Drafted and Reviewed by: Alaa AlDoh, Adam Parker, Jamie P. Cockcroft, Julia Wolska, Yu-Fang Yang, Charlotte R. Pennington

Note that we are currently working on an automated mechanism to link references cited above with their full-length version that can be found at with all references used so far.