I’ve always been curious about the white-person McDonalds process, myself.

]]>Yes! As the abstract says, a good portion of the talk will be addressing the question: But what exactly are “nonparametric” priors?

As Mikhail suggests, it often involves introducing quite a lot of parameters (often more parameters than observations, in fact) and then trying to come up with reasonable priors over this high-dimensional set.

There are many working definitions of nonparametric. Parametric models are often described as having finitely many unknowns/parameters. On the other extreme, nonparametric models are often described as having unknowns that live in infinite-dimensional spaces. Loosely speaking , we can understand nonparametric Bayesian models as a class of models that make few restrictions on the structure of the unknown.

]]>“Nonparametric” is one of the most confusing term in Statistics. Its related to Gaussian processes and stuff, and it used quite a lot of parameters to be honest.

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