For a family of distribution \, \{ \textsf{Ber}(p)\} _{p\in (0,1)} \, , Jeffreys prior is proportional to:

Question

\, \pi _ j(p) \propto \,
unanswered

Therefore, the Jeffreys prior is:

Proper

Improper

Answer 1

The Jeffreys prior for a family of distributions is typically proper, meaning it integrates to a finite value and can be used for Bayesian inference.