Statistics is a scary subject. How do I know? Well it definitely scared me! After all, a lot of it seems relatively ad hoc . The reason for that, of course, is that it really is pretty ad hoc ! But perhaps it doesn't have to be. That's where the Bayesian perspective (as opposed to the frequentist) really shines: it provides a rational foundation for the fundamental task of inference. Here on this site I hope to provide some articles about common (and uncommon) tasks of inference. I might put up some of my other work too (I am a grad student after all). I hope you'll find it useful!
Andrew Marantan is currently a physics graduate student at Harvard University working with Professor L. Mahadevan. He is currently interested in how animals (even highly-evolved ones like humans) engage in tasks of inference and control, including strategy. He's also particularly interested in Bayesian inference and clarifying statistics through Bayesian reasoning. His current problems of interest involve catching balls, shooting cannonballs, dung beetles rolling dung balls, and archerfish shooting and catching prey (were you expecting another ball?). He also doesn't usually write in the third person but thought it immodest to write about himself otherwise.
Contact Info:Andrew Marantan