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: Bayes Factor, Bayesian inference, Bayesian parameter estimation, Likelihood function, Prior distribution
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
We are currently working to link the references directly. For now, the complete reference list can be viewed here.