(iv) I recall reading a pretty good blog post a year or two ago that described how to implement some kind of Bayesian token recognition thing to parse screen captures from some database (or something roughly like that). The gist of the approach was like this:
1. define a model expressing that certain combinations of neighbouring tokens are more likely to occur than others
2. approximate the full Bayesian inference problem as MAP inference
3. the resulting combinatorial optimisation problem could be encoded as a relatively easy mixed integer program
4. easy mixed integer programs are very tractable to commercial solvers such as CPLEX, Gurobi, or sometimes even the open source COIN-OR CBC
At the time I found the idea fascinating as I was working with LPs/MIPs and had some interest in Bayesian inference, but hadn't figured out that the former could provide a way to computationally tackle certain approximations of the latter.
I cannot for the life of me find the link again for this.
(i) MacKay's book on Information Theory, Inference, and Learning Algorithms: http://www.inference.phy.cam.ac.uk/itila/
(ii) Probability Theory As Extended Logic: http://bayes.wustl.edu/
(iii) Causal Calculus: http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...
(iv) I recall reading a pretty good blog post a year or two ago that described how to implement some kind of Bayesian token recognition thing to parse screen captures from some database (or something roughly like that). The gist of the approach was like this:
1. define a model expressing that certain combinations of neighbouring tokens are more likely to occur than others 2. approximate the full Bayesian inference problem as MAP inference 3. the resulting combinatorial optimisation problem could be encoded as a relatively easy mixed integer program 4. easy mixed integer programs are very tractable to commercial solvers such as CPLEX, Gurobi, or sometimes even the open source COIN-OR CBC
At the time I found the idea fascinating as I was working with LPs/MIPs and had some interest in Bayesian inference, but hadn't figured out that the former could provide a way to computationally tackle certain approximations of the latter.
I cannot for the life of me find the link again for this.