The Jeffrey’s prior is an easy-to-compute reference prior that is
invariant to transformation, used in Bayesian Inference. If the model
only has a univariate parameter \(\theta\), the prior is given by:

\begin{equation}
p(\theta) \propto \sqrt{I(\theta)}
\end{equation}

where \(I(\theta)\) is the expected Fisher information in the model.

If \(\mathbf{\theta}\) is multi-dimensional, then the Jeffrey’s prior is
given by:

\begin{equation}
p(\theta) \propto \sqrt{\operatorname{det}\{l(\theta)\}}
\end{equation}

where I is the Fisher information matrix. When the number of
dimensions is large, this method becomes cumbersome. A common approach
is to obtain non-informative priors for the parameters individually,
and form the joint prior as a product of these individual priors.

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