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Yeah, but a relevant measure of complexity is not that easy to come by. And by what measure do you judge which is more 'valid'? For example, AIC, BIC etc. may h

by woodson 15y ago

Yeah, but a relevant measure of complexity is not that easy to come by. And by what measure do you judge which is more 'valid'? For example, AIC, BIC etc. may have a strong effect on which model 'wins'. You are right of course, but I'm wary of making claims based on inferences from model comparisons without stating such limitations.

6ren 15y ago
Yes, measure of model complexity is a problem. A solution is to use a program for a Turing machine as the model. The length of the program represents the complexity (which measures the choices that form the program). There's different formulations of a turing machine (different languages for programming one), but since any turing machine can be simulated by any other turing machine with a program, the length of that program becomes the error (that represents the difference in complexity introduced by your selection of some specific Turing machine). If you are comparing models using the same turing machine, then this constant difference doesn't matter. a-b = (a+x) - (b+x) One practical solution is that more data swamps minor differences between the specification language used to describe models. More data is better.