Nathaniel Egwu writes:
I am a PhD student working on machine learning using artificial neural networks . . . Do you have some recent publications related to how one can construct priors depending on the type of input data available for training? I intend to construct a prior distribution for a given trade-off parameter of my non model obtained through training a neural network. At this stage, my argument is due to the fact that Bayesian nonparameteric estimation offers some insight on how to proceed on this problem.
As I’ve been writing here for awhile, I’ve been interested in weakly informative priors. But I have little experience with nonparametric models. Perhaps Aki Vehtari or David Dunson or some other expert on these models can discuss how to set them up with weakly informative priors? This sounds like it could be important to me.