This general idea can also be identified in a rather new field on the intersection of Bayesian statistics and machine learning, Bayesian nonparametrics (e.g., Orbanz and Teh 2010, Hjort et al 2010). Rather than specifying, at the outset, a confined set of distributions from which a statistical analysis is supposed to choose on the basis of the data, the idea is that the data are confronted with a potentially infinite-dimensional space of possible distributions. The set of distributions taken into consideration is then made relative to the data obtained: the complexity of the model grows with the sample. The result is a predictive system that performs an online model selection alongside a Bayesian accommodation of the posterior over the model.