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I think there's nuanced distinctions between: - something that can't be modeled because there's no training data - something that can't be modeled because it'

by pizza 2y ago

I think there's nuanced distinctions between: - something that can't be modeled because there's no training data - something that can't be modeled because it's fundamentally stochastic - something that can't be modeled because the discrepancy in simulating the generating process, for your specific model, can, basically, be made arbitrarily large

paulmd 2y ago
why can fundamentally stochastic things not be modeled? monte carlo simulations were literally some of the first computer programs.
- pizza 2y ago
  can't be modeled is probably not what I should have said, rather I meant more like the error for a globally optimal model is still high