A gentle introduction to statistical relational learning

Can anyone explain to me how the "partition function" 'Z' is calculated? That part is a little glossed over in the post and the description in the paper is a little beyond my immediate understanding. :)

Partition functions are usually hard to compute as they require a sum or integral over the entire parameter space. In this case, it's "Z is a normalizing constant, that is: it's the sum of the values of all possible assignments."

So perhaps sum_x { exp( sum_i { w[i] * g[i](x) } ) }

tel is 100% correct.

And you're right, it is a little glossed over, I'll try to be more explicit. Thanks for the comment!

reminds me of Progol :http://www.doc.ic.ac.uk/~shm/progol.html

FTA:

∀x:Add(x,0)=0

Whut? Shouldn't it be

∀x:Add(x,0)=x ?

Ugh! That was silly... It's fixed now, thanks!