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, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in Psychology, 5, 781. https://doi.org/10.3389/fpsyg.2014.00781
- Lüdtke, O., Ulitzsch, E., & Robitzsch, A. (2020). A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models with Small Sample Sizes . https://doi.org/10.31234/osf.io/u3qag
Originally drafted by: Alaa AlDoh
Reviewed by: Adam Parker, Jamie P. Cockcroft, Julia Wolska, Yu-Fang Yang, Charlotte R. Pennington