Abstract
The problem of learning graphical models from iid data is widely studied, but unfortunately strong computational lower bounds are known when there are higher order dependencies. I will show how assuming the data is generated by a natural process called the Glauber dynamics allows us to circumvent these barriers, by harnessing the dependencies. This is based on joint work with Jason Gaitonde and Elchanan Mossel.