Is it a good idea to use the mean and standard deviation of coefficients from other models as my prior?

Hi, 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.

Or if different use cases, when does it make sense to use one or the other?

Thanks in advance!

Most of the methods you describe won’t all have the same number or type of parameters. Are you perhaps asking about what happens if you have, say, coefficients from a ridge regression along with those from an ordinary least squares?

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hi ckrapu

No, actually all these methods can be used to give linear coefficients that can be used as a solution to ordinary least squares.

The coefficients computed by all of these, along with the closed form solution (normal equations) are what I used to compute mean and standard deviation for each parameter’s coefficients.

So in detail. I used the LinearRegression, Ridge, Lasso, ElasticNet, LinearSVR models and the closed form solution to compute coefficients for a solution to my regression problem.
I combined some of these with bagging and stacking.
For NN I used keras and a single layer, linear activation.

I verified each of the coefficients by multiplying X@w (w is the weights of the respective models) to compute errors, they are all within .02 of one another for a dataset of about 700 samples.
There is a lot of redundancy in the data and thus the solutions (weight vector varies) quite a bit. So standard Deviation is quite large.