The more complex methods can support multiple hierarchi- cally organized layers of inference, struc- tured representations of abstract knowledge, and approximate methods of evaluation that can be applied efficiently to data sets with many thousands of entities. For the first time, we now have practical methods for developing computational models of human cognition that are based on sound proba- bilistic principles and that can also capture something of the richness and complexity of everyday thinking, reasoning, and learning.