### Bayesian Latent models: the shortest explanation ever

I found this on wikipedia and this is presumably the best and shortest explanatation I ever found about latent variable models and especially these Bayesian non-parametric models:

I let you think about the second definition, but think about an infinite collection of labels you can put or not on each object wheter it has the feature or not (and maybe my explanation is not as clear as the definition from wikipedia)

- The
**Chinese Restaurant Process**is often used to provide a prior distribution over assignments of objects to latent categories. - The
**Indian buffet process**is often used to provide a prior distribution over assignments of latent binary features to objects.

I let you think about the second definition, but think about an infinite collection of labels you can put or not on each object wheter it has the feature or not (and maybe my explanation is not as clear as the definition from wikipedia)

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