AI engineering glossary · Statistics inference
What is posterior distribution?
Posterior distribution: The probability distribution of a parameter after combining the prior with observed data through Bayes' theorem. It holds everything the analysis knows about the parameter and is used for estimates and intervals.
Posterior distribution: an example
A Beta(2, 38) prior updated with 30 conversions and 470 non-conversions gives a Beta(32, 508) posterior.
Related terms
More Statistics inference terms
- Potential outcomes framework
- Power analysis
- Practical significance
- Prior distribution
- Propensity score
- R-squared
- Randomisation
- Randomised controlled trial
Where you learn it
Posterior distribution is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.