Zoubin Ghahramani, Center for Automated Learning and Discovery, CMU
I will briefly review the discussion of infinite models from my Feb 4
ML Lunch. I will then focus attention on two novel types of infinite
models. Infinite Mixtures of Experts are a model that can be used for
regression in situations where Gaussian Processes may be
inadequate. Infinite-state HMMs are a nonparametric form of HMM in
which the number of required hidden states, architecture, etc are
determinined automatically. Both models make use of Markov chain
techniques to sample over the latent variables while implicitly
integrating out the (infinitely many) model parameters.
Some small applications will be used to demonstrate these models.
Finally, I will return to the question posed in the Feb 4 talk: should
one use Occam's razor to find simple models, or should one ignore
Occam's razor and focus on very large models?
Joint work with Carl E Rasmussen and Matthew J Beal.