A prior could be a population level statistic which is then updated by a likelihood to reveal a posterior probability. Take for example a medical test. We know that on average that 1/10 people have some disease X. We have a test that tests for X (with some false positive rate).. Using that we can calculate the posterior probability that you actually have disease X. If you relied just on the test you don't discount the population level statistics which might be relevant. You can also use the tests FP rate in the same way as your prior... e.g. the people who test positive who don't have the disease etc...
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A prior could be a population level statistic which is then updated by a likelihood to reveal a posterior probability. Take for example a medical test. We know that on average that 1/10 people have some disease X. We have a test that tests for X (with some false positive rate).. Using that we can calculate the posterior probability that you actually have disease X. If you relied just on the test you don't discount the population level statistics which might be relevant. You can also use the tests FP rate in the same way as your prior... e.g. the people who test positive who don't have the disease etc...