miltava
Joined 2 years ago
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Apr 19
Joined 2 years ago
The idea is that a complex model explains many more configurations (datasets) than a simple one. So its (prior) probability distribution is lower on the data seen (to compensate for the other possibilities it might explain). So the (marginal) likelihood that the simple model is correct is higher if it fits the data well enough.