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I have a dataset that I’ve been playing around with for school I have gotten very good results with a bunch of methods (Ridge, Lasso, ElasticNet, SVM, Bagging, Stacking and NN even)

Now I’m having a range of different coefficients of my predictors, is it a good idea to use them as my priors (I did so, I think the result has been ok) or should I use noninformative priors instead.

If it is a bad idea, could you explain why?

Or if different use cases, when does it make sense to use one or the other? I'm using Pymc3's sampler and GLM.

Thanks in advance for any pointers and explanations.

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    $\begingroup$ bayesian inference allows for subjective information (based on good grounds) to be included as prior, doesnt it? $\endgroup$
    – Nikos M.
    Mar 26 at 17:37
  • $\begingroup$ yes, that's why I thought it's a good idea to include them as prior, but now I have doubts ? @NikosM. $\endgroup$
    – Oliver
    Mar 26 at 17:46

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