Can Bayesian inference always be done with improper priors? If not, when can it be done?
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Approach this question by first understanding what an improper prior is. Remember that improper priors are those that do not integrate to one and thus cannot represent valid probability distributions. Focus on the conditions under which these priors can lead to proper posteriors. It's also important to discuss when it is inappropriate to use improper priors due to the possibility of yielding non-normalizable posteriors.
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