Jeffreys' prior is a non-informative prior distribution used in Bayesian statistics that is derived from the likelihood function of a statistical model. It is designed to be invariant under reparameterization, making it particularly useful for inference where prior knowledge is minimal. This type of prior is often used to express uncertainty without biasing the results, thereby facilitating likelihood ratio tests and overall Bayesian inference processes.
congrats on reading the definition of Jeffreys' prior. now let's actually learn it.