I initially wanted to convey the difficulty of modeling physical systems by featuring some examples I knew something about early on the Robust Mathematical Modeling blog. Instead, a reader of the blog, Nicolas Kuehn kindly provided an example he is working on. Where is this discussion going ? I am not sure but I don't see discussions like these often on the web. Many disciplines in engineering and science are facing the same type of problems, a mild case of curse of dimensionality with nonlinear models and a dearth of expensive data. There are currently 95 people reading the blog through an RSS feed and there is an accompanying LinkedIn group. The more I think about it, the more I think some of the machine learning techniques such as manifold learning ought to be investigated alongside the generic model building capabilities generally deployed with traditional Bayesian statistics. These methods should eventually be part of the bag of tools engineers and scientists in the trenches, use. I mean it's great to do classification on images, video and text but what about doing this for real data and making sense of them ? Do you want to be part of that discussion ? I thought so, invite your friends, they're welcome too...
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