Bayesian techniques are effective with small data sets if you build your model carefully. For example, if you are able to encode the generating process of your

Bayesian techniques are effective with small data sets if you build your model carefully. For example, if you are able to encode the generating process of your dataset, even a _single_ example could be used for effective training. For example, see this http://web.mit.edu/jgross/Public/lake_etal_cogsci2011.pdf http://web.mit.edu/jgross/Public/lake_etal_cogsci2011.pdf By providing a good generative model, you are shifting the effort from collecting more data to carefully coding your expert knowledge. In my view, this is an exciting paradigm as it allows combination of human knowledge (as generating process and prjors) that can be overcome (in posterior distributions) if enough data is accumulated against it.