Bayesian statistics offers rich rewards, including that it gives you a probability distribution for everything. But there’s always the pesky question of how to define what’s called the prior probability distribution, especially when we don’t have much information to go on. In such cases, we usually try to define a “non-informative” (or maybe “non-informed”) prior, i.e., one which doesn’t make any assumptions, and has the smallest possible impact on the final answer so we can let the data speak for themselves.
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