Michael P. Verdicchio

Over the summer and this semester I have been endeavoring to teach myself about Bayesian networks and their usability in modeling biological systems, and specifically gene interactions. The task has proved to be a difficult one, as my approach has not been very structured. It started with going through the 1995 tutorial by David Heckerman, but with each step I’ve had to make side journeys to fill in gaps in background knowledge.

So far in our lab seminar series (http://sysbio.fulton.asu.edu) I’ve covered the background in probability theory and in graph theory (Markov chains/blankets/equivalence, faithfulness, d-separation, etc.), and have begun discussing how to actually learn Bayesian networks from data. The last thing discussed was an intro on leaning posterior probability distributions, which will lead into learning network topology, and finally learning both.

I am posting my slides/notes from the four talks I’ve given, but take them only as an illustration of my progress and not as a good resource. For good resources, check out David Heckerman, Nir Friedman, Dana Pe’er, Eran Segal, Marco Ramoni, Andrew Moore, Jose Pena, and Daphne Koller, Richard Neapolitan, and of course the seminal text by Judea Pearl. All of those are who I am learning from.

Talk 1 (ppt) (horrible introduction, I didn’t know anything)

Talk 2 (ppt) (getting there, decent probability discussion)

Talk 3 (ppt) (getting better, decent graph discussion)

Talk 4 (pdf) (not so great, mostly review, notes only, no slides used)