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  "body": "\n## Summary\n\nA [[statistical model|K3AMMSY8DD]] is identifiable when different parameter values give different laws of the data.\nThen the law of the data determines the parameter, and it makes sense to estimate the parameter.\nWithout identifiability no amount of data can separate parameters that give the same law, and consistent estimation of them is impossible.\n\n## Explanations\n\n### L1 human\n\nImagine that you see only the sum of two unknown numbers plus some noise.\nYou can learn the sum, but you can never learn the two numbers apart, since $1 + 2$ and $2 + 1$ give the same data.\nSuch a model is not identifiable.\nIn an identifiable model each parameter value leaves its own fingerprint on the distribution of the data.\n",
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