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Those papers are very interesting. I was particularly surprised that the method exactly recovers the Big-5 components of personality from the results of the sur
by Xcelerate 11y ago
Those papers are very interesting. I was particularly surprised that the method exactly recovers the Big-5 components of personality from the results of the survey questions.
The whole idea of "simplest model that explains most of the data" has always been very appealing to me. The concept is closely tied to reproducing kernel Hilbert spaces, which have recently experienced a revival in interest due to the representer theorem.
- eli_gottlieb 11y ago>I was particularly surprised that the method exactly recovers the Big-5 components of personality from the results of the survey questions. Why? Is that a standard dataset? They could have just cherrypicked tasks. I also noticed that they seem to only have the actual computation working for discrete random variables (integer features) right now, which limits its applicability. They also seem to use mass-function estimators, which again can work well for discrete data while becoming statistically intractable when dealing with continuous random variables. >The whole idea of "simplest model that explains most of the data" has always been very appealing to me. In this case it's a bit more like, "The set of latent variables that best screen off the observables from each-other."
- emcq 11y agoYou will probably be interested in Kolmogrov complexity, which studies this using an information theoretic lens. However it is just an abstract concept and not really something you can measure precisely.