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  "body": "\n## Summary\n\nA prior probability is the probability that we give to a hypothesis before we see the data.\nMore generally, a prior distribution is the distribution that we give to an unknown quantity before we see the data.\nIn [[Bayes' theorem|0NM6E9VW04]] the prior is combined with the likelihood of the data, and the result is the [[posterior probability|0KJ7ZS8AJA]].\n\n## Remarks\n\nAn informative prior uses real knowledge about the quantity. An uninformative prior tries to express little knowledge, for example a uniform distribution on the possible values.\nAn improper prior is a \"distribution\" with infinite total mass, such as the uniform measure on $\\mathbb{R}$. It can still give a proper posterior.\n\n## Explanations\n\n### L1 human\n\nThe prior is what you believe before the experiment.\nIf $1\\%$ of people have a disease, then the prior probability that a random person is sick is $0.01$, before any test is done.\n",
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