Non-informative priors are used in Bayesian Inference, in the
situation where no prior information exists, or inference based
dominantly on the data is desired. We wish to find a distribution
\(p(\theta)\) that contained “no information” about \(\theta\) (favours no
value of \(\theta\) over another).

An example of such a non-informative prior in the discrete space would
be a uniform distribution over \(\theta\).

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