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Nonparametric Bayesian


A nonparametric mode is:

  1. A parametric model with #parameters that increases with the #data.
  2. A really large param model;
  3. A model over infinite dimensional function/measure spaces;
  4. A family of distributions that is dense in some large space.
Why nonparam models is better?
  1. “allows the data speak for itself”! board class of priors!
Victor’s Side note:
Nonparametric Bayesian’s advantage:
imagine you don’t know the number of modes in a distributions, e.g. K Gaussian distributions but K is unknown.
Using parametric way, you need to pin down one K as hypothesis then test it. Pick the optimal one K.
Well in nonparam, K can be a random variable, and the P(x | K) can be approximated by MLE/MAP.
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