Posterior

In Bayesian inference, the posterior distribution represents the updated probability distribution of a parameter after observing new data. ¹

From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a scientific hypothesis, or parameter value), given prior knowledge and a mathematical model describing the observations available at a particular time. ²

References

1. Posterior probability - Wikipedia ↩

2. Grossman, Jason (2005). Inferences from observations to simple statistical hypotheses (PhD thesis). University of Sydney. hdl:2123/9107 ↩