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In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with large spread is used when little is known about the parameter(s), while a more narrow prior distribution represents a greater degree of information.

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I came across a question 8 at the end of chapter 3 of the book: "Give two simple examples showing a case in which a prior distribution would not be overwhelmed by data, regardless of the sample … size" Can anyone provide any examples where this would be the case, because I thought that as the number of data points goes to infinity, the data will always overwhelm the prior (assuming no ill-defined priors). …
asked Jan 13 '18 by AlexMe
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This article$^1$ on p.16 specifies Haldane's prior as: $$p(\theta) = \frac{1}{θ(1−θ)}$$. However, other$^2$ source on p.6 specifies Haldane's prior as proportional to $\frac{1}{θ(1−θ)}$, i.e. $$p …
asked Jan 31 '18 by AlexMe