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We use priors in Bayesian networks to include prior knowledge in our models. In this context, what are these two terms:

-complicated prior -large scale prior

I have seen priors like Laplace, zero-mean Gaussian, Jaffery's or truncated Gaussian priors. However, the above two terms are new to me.

Can somebody point me towards a tutorial on this?

Tx

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    $\begingroup$ Could you point to reference that used those terms? $\endgroup$
    – Tim
    May 31 '21 at 13:35
  • $\begingroup$ Actually it was an email from my senior colleague, and for some reason, his responses are very late. He is trying to say that we can should focus on "online learning", for large scale learning, may be using a large scale or a complicated prior in our Bayesian classification algorithm. $\endgroup$ May 31 '21 at 14:42

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