4 ms·
Basically he brought Bayesian Networks to machine learning. He did a lot in their structure and in particular efficient/approximate inference on Bayesian Netwo
by Dn_Ab 15y ago
Basically he brought Bayesian Networks to machine learning. He did a lot in their structure and in particular efficient/approximate inference on Bayesian Networks. He has been contributing brilliant ideas since the early 80s I think.
Search belief propagation, junction tree algo, markov blanket, belief propagation statistical physics. The last search showing his ideas finding use beyond where he introduced them. Whenever ideas keep showing uses in different places especially in something as well evinced as the thermodynamic/information link then you know you have done something profound.
- ArbitraryLimits 15y agoA few years ago I looked up his earliest papers, and it's interesting that his work on Bayesian networks essentially seemed to start as an effort to detect causality among sets of random variables, i.e. given a Bayesian network, which random variables can be said to "cause" others? Of course, Bayesian networks start by assuming you know every random variable's distribution, so verifying independence is easy but finding it is hard. I've always thought an interesting research project would be to try the same approach with correlation functions instead of distributions, and see how far you can get.