A non-informative prior is a type of prior distribution that provides minimal information about a parameter before observing any data. It is often used to express a state of ignorance or neutrality regarding the value of the parameter and allows the data to have a stronger influence on the posterior distribution. This concept is crucial when one seeks to let empirical evidence drive the inference process, minimizing biases introduced by subjective beliefs.
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