| 释义 |
Bayesian inference In Bayesian statistics, parameters have probability distributions, while in frequentist statistics parameters have fixed values. In Bayesian inference, a prior distribution is proposed for a parameter and after further data is collected Bayes' Theorem is used to calculate a posterior distribution in light of the new data. Frequentist inference, in contrast, would use a hypothesis test to assign a p-value to a null hypothesis that the data came from a population with a suggested parameter value.
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