Michael P. Verdicchio

I recently included a section on d-Separation in my most recent talk on causality, but I wanted to give it its own post. Before defining it formally, a brief history is given here from Richard Scheines’s page at CMU (http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html).

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This semester we’ve covered a number of topics in Sungwook Yoon’s Statistical Relational Learning reading group. My turn came a couple of weeks ago and I presented Bayesian Logic Programming. It is essentially a methodology which combines the structural conveniences of Bayesian networks and the theorem proving aspects of logic programming. The chapter in the Getoor/Taskar text was not especially liked in the group as it lacked, among a number of things, specific examples of its use and advantages; in other words, we didn’t know WHY we should use such a framework, only that it was an interesting hybrid of two seemingly separate methodologies. Nonetheless, my slides are below.

Slides (ppt)

In the last Computational Systems Biology lab group seminar I presented the topic of causality. It was essentially a survey of the first two chapters of Judea Pearl’s book, Causality. The slides for the talk can be found at the link below.

Causality Seminar Slides (ppt)