Wednesday, October 17, 2007

Bayes net and phrase structure

Note to myself before it disappears from my head:

From http://www.cscs.umich.edu/~crshalizi/notebooks/graphical-models.html

"Graphical models are, in part, a way of escaping from this impasse.

The basic idea is as follows. You have a bunch of variables, and you want to represent the causal relationships, or at least the probabilistic dependencies, between them. You do so by means of a graph. Each node in the graph stands for a variable. If variable A is a cause of B, then an arrow runs from A to B. If A is a cause of B, we also say that A is one of B's parents, and B one of A's children. If there is a causal path from A to B, then A is an ancestor of B, and B is a descendant of A. If a variable has no parents in the graph, it is exogenous, otherwise it is endogenous."

Could phrase structure / c-commanding in fact be the derivatives of causality?

3 comments:

Robin S. said...

Ok, paca. I've done graphs, I understand the concpet of manipulating variable in experiments, etc., but you've thrown me a curve ball, here. Can we have more background? Sort of as a story, like the Wahoo moment had?

pacatrue said...

Ha-ha. Sorry, robin s. I probably shouldn't have posted this one at all. It's me note-taking. I had an idea I didn't want to lose and if I stuck it in some file on my pc I'd never see it again, so I stuck it here on the blog.

I will in the future try to write this up, but honestly, I'm no expert on this one either. It relates to my wahoo post, hence the bit about causality, but I'm pretty clueless about Bayes' nets, too. Anyay, I will follow your instructions in the future.

Robin S. said...

Oh, honey - no instructions!

I was just doing a little begging - we just expect to learn when we visit you, you know. It's fun.