The talk introduces a new framework for learning probability density functions by assessing their performance against a set of 'test functions'. A theoretical analysis suggests that we can tailor a distribution for a class of tasks by training it to fit a small subsample. There is a trade-off between the complexity of the class of distributions used for the distribution and the complexity of the set of tasks. Experimental evidence is given to support the theoretical analysis.