A stochastic programming perspective on nonparametric Bayes

author: Daniel Roy, Linguistics and Philosophy, Massachusetts Institute of Technology, MIT
published: Aug. 4, 2008,   recorded: July 2008,   views: 328
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

We use Church, a Turing-universal language for stochastic generative processes and the probability distributions they induce, to study and extend several objects in nonparametric Bayesian statistics. We connect exchangeability and de Finetti measures with notions of purity and closures from functional programming. We exploit delayed evaluation to provide finite, machine-executable representations for various nonparametric Bayesian objects. We relate common uses of the Dirichlet process to a stochastic generalization of memoization, and use this abstraction to compactly describe and extend several nonparametric models. Finally, we briefly discuss issues of computability and inference.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: