Good if make prior after data instead of before
Summary
LessWrong's Good if make prior after data instead of before argues that priors should be informed by information before observing data, but in practice refining priors after seeing data and discretizing the hypothesis space can lead to more robust inferences. It also critiques empirical Bayes and discusses the challenges of infinite possible truths, proposing pragmatic categorization and data-driven updating.