Machine learning researchers often have to contend with issues of model selection and model fitting in the context of large complicated models and sparse data. The idea which I am pushing for in this project is that these can be nicely handled using Bayesian techniques.

Model selection is selecting, among a class of models each of which has finite capacity, the model of the right capacity. Nonparametric Bayesian modelling sidesteps model selection by simply using models of potentially unbounded (or infinite) capacity. Overfitting is avoided simply by the usual Bayesian approach of integrating out all parameters (perhaps using MCMC or variational methods).