Chapter 4 Findings: Jeffreys Prior

Let \(\theta\) be a parameter of a distribution that model a random variable \(X\):

\(\pi(\theta)\) denotes the prior of \(\theta\);

\(\pi_J(\theta)\) denotes the Jeffreys Prior of \(\theta\);