By the name, noninformative prior, the prior distribution doesn't contain any information about the parameter. Then why we use this thing to estimate the parameter by the Bayesian approach?
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The purpose of using a non informative prior is to utilize a more objective approach. Non-informative priors allow the data to have a greater influence on the posterior. Though, if you have enough data, your choice of prior is not really going to matter.
That said, the term non-informative can be misleading. A uniform distribution does input information into a model since it provides equal probability weight to extreme values (imagine SAT scores, a non-informative prior would say that a perfect score is just as likely as an average score on the scale).
In my own research, I have bought into McElreath's line of reasoning and use semi-informative priors. These are priors that work more to regularize results rather than input any information into a model. I find them more useful than a non-informative prior when modeling a parameter that I do not have strong prior information about.
