Nice high level talk on Statistical Inference for Big Data by Jordan. Been one of my favorites since his LDA/PLSA papers in 2003 with Andrew NG.
http://videolectures.net/colt2014_jordan_bigdata/?q=jordan
An Introduction to Probabilistic Graphical Models (2003) [pdf]
31–37 of 37 posts
Re: An Introduction to Probabilistic Graphical Models (2003) [pdf]
#32Note there's an exploding literature that reads these models as causal models: http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf
Re: An Introduction to Probabilistic Graphical Models (2003) [pdf]
#33Earlier quoted context omitted.
Do they train with backpropagation efficiently?
No, back-propagation would not give full Bayesian inference (although there are some tricks [0]). They instead use variational inference[1], which allows for fast inference of continuous PGMs. [0] http://mlg.eng.cam.ac.uk/yarin/blog_2248.html [1] https://arxiv.org/abs/1603.00788
Most variational inference are not full-Bayesian as well...
Re: An Introduction to Probabilistic Graphical Models (2003) [pdf]
#34Earlier quoted context omitted.
Yes, although in directed acyclic graphs the 'cycles' manifest as multiple paths to a node.
cycles can be handled in two ways: if you are happy with approximate solutions, loopy BP can give that (still linear, but may take longer and there's parameter tuning), for exact solutions you can rewrite the graph to "carry" dependencies (latest paper by Frey)
Could you link to that paper? And does it have anything to do with the junction tree algorithm?
Re: An Introduction to Probabilistic Graphical Models (2003) [pdf]
#35Earlier quoted context omitted.
cycles can be handled in two ways: if you are happy with approximate solutions, loopy BP can give that (still linear, but may take longer and there's parameter tuning), for exact solutions you can rewrite the graph to "carry" dependencies (latest paper by Frey)
Could you link to that paper? And does it have anything to do with the junction tree algorithm?
http://www.psi.toronto.edu/~psi/pubs2/1999%20and%20before/13...
I don't know about junction trees but it probably connects as junction trees are a generalization of factor graphs
Re: An Introduction to Probabilistic Graphical Models (2003) [pdf]
#36Earlier quoted context omitted.
Could you link to that paper? And does it have anything to do with the junction tree algorithm?
http://www.psi.toronto.edu/~psi/pubs2/1999%20and%20before/13... I don't know about junction trees but it probably connects as junction trees are a generalization of factor graphs
Thanks!
Re: An Introduction to Probabilistic Graphical Models (2003) [pdf]
#37Earlier quoted context omitted.
cycles can be handled in two ways: if you are happy with approximate solutions, loopy BP can give that (still linear, but may take longer and there's parameter tuning), for exact solutions you can rewrite the graph to "carry" dependencies (latest paper by Frey)
Could you link to that paper? And does it have anything to do with the junction tree algorithm?
the Christopher Bishop chapter on graphical models has a good section on junction trees IIRC