Now that you mention it, one of the reasons I'm trying to get acquainted with the methods Thrun uses is to see how much they rely on advance knowledge of exactly how the sensor works (i.e. its true likelihood function). Then, I want to see if it's possible to infer enough relevant information about the likelihood function (such as through unsupervised learning) so that I can design a program that doesn't have to be given this information about the sensors.
And that's starting to sound more similar to what you would want to do.
That'd be interesting. More posts on the real world use of bayesian models would be good for lesswrong I think.
But I'm not sure how relevant to my problem. I'm in the process of writing up my design deliberations and you can judge better once you have read them.
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