Here are my highlights from CBMS: the non-parametric Bayes conference at UC Santa Cruz. It was organized more like a summer school, however.

The conference was dominated by Peter Muller, who gave 10 1.5 hour lectures on non-parametric Bayes. He talked mainly of Dirichlet processes and the generalizations to them: Pitman-Yor, Polya trees, ect. He presented a "graphical model of graphical models" demonstrating the connection between the related models. He went through each model and compared them by their predictive probability function (PPF), which is the one-step-ahead predictive distribution for the models. Notably absent from his unifying view was Gaussian processes.

Michael Jordan gave one lecture where he went through various models various NP Bayes models he has worked with: LDA, IBPs, sticky HMMs, ... He didn't get too technical, but tried to give a high level view of many models motivated by applications such as speaker diarization.

Wes Johnson gave one lecture giving examples of NP Bayes in biology.

Finally, Peter Hoff gave one lecture "Alternative approaches to Bayesian nonparametrics". He gave some examples of how doing Bayesian inference with an unknown Gaussian has a better predictive probability than using a DP-mixture for N <> 100 were referred to "large" and N > 5000 as "huge".

The slides are available here:

http://www.ams.ucsc.edu/notes

Elastic net, LASSO, and LARS in Python

4 years ago

## 1 comment:

I read one more article related to the same topic but I must say that the way you wrote this makes it more interesting and this one is also more detailed.

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