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I'm not sure why that gif is so interesting to you; that wasn't the reason I submitted this piece. Average and standard deviation are hardly useful data points
by Invictus0 9y ago
I'm not sure why that gif is so interesting to you; that wasn't the reason I submitted this piece. Average and standard deviation are hardly useful data points in evaluating a dataset, and also don't have anything to do with probability theory. The second half of the post contains the bulk of its interesting content (as I read it).
- macawfish 9y agoMaybe I'm missing something? To me, that gif illustrates sets of samples that are on the surface compatible with the the same Gaussian distribution, even if a Gaussian distribution is "obviously" lacking.
- wenc 9y agoI think 0xBABAD00C's comment is on the money. The gif is almost a non-sequitur and does not seem to support the conclusion the article is trying to reach. The second part of the article makes a claim that is worth discussing -- that probabilistic inference does not work well in nonlinear domains. I think this is true in one sense, but in many practical situations we can usually to get something to work well enough for it to be useful. Assuming we can (approximately) split up the domain into multiple regions and get probability distributions for each region (with transition functions between them), we can continue to make inferences under nonlinearity. That said, this is a concession. Perhaps there are techniques in deep learning that more general in nature.