Is there a metholodgy for bayesian analysis that avoids the choice of a specific prior but instead provides conclusions in the form of boundary/regions in "prior-space" and their effect on belief? For example, it would be incredibly useful if the output of research allowed a reader to gauge support of the conclusion in a minimally subjective way by explaining what effect choices in prior have on results. I'm not a statistician so I'm assuming this is a well understood thing, but would be curious to know if and how it is practiced.
Generally speaking, a good Bayesian analysis includes what's known as a "sensitivity analysis" which seeks to measure how sensitive the results are to the particular choice of prior. Additionally, if strong prior assumptions are not available, an "uninformative" prior is used. In such cases, the results tend to be pretty close to those from frequentist methods, except the frequentist methods lack the Bayesian probabilistic interpretation.
The final result of a Bayesian analysis is the posterior probability, which is the product of the prior probability (which can incorporate previous knowledge or convictions) and the likelihood (which is determined by the data you just collected). You can leave out the prior, and just report the likelihood. Another comment mentions this: https://news.ycombinator.com/item?id=17438643
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Is there a metholodgy for bayesian analysis that avoids the choice of a specific prior but instead provides conclusions in the form of boundary/regions in "prior-space" and their effect on belief? For example, it would be incredibly useful if the output of research allowed a reader to gauge support of the conclusion in a minimally subjective way by explaining what effect choices in prior have on results. I'm not a statistician so I'm assuming this is a well understood thing, but would be curious to know if and how it is practiced.
Generally speaking, a good Bayesian analysis includes what's known as a "sensitivity analysis" which seeks to measure how sensitive the results are to the particular choice of prior. Additionally, if strong prior assumptions are not available, an "uninformative" prior is used. In such cases, the results tend to be pretty close to those from frequentist methods, except the frequentist methods lack the Bayesian probabilistic interpretation.
The final result of a Bayesian analysis is the posterior probability, which is the product of the prior probability (which can incorporate previous knowledge or convictions) and the likelihood (which is determined by the data you just collected). You can leave out the prior, and just report the likelihood. Another comment mentions this: https://news.ycombinator.com/item?id=17438643