{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T07:37:34Z","timestamp":1785569854626,"version":"3.56.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper outlines a framework for quantifying the prior\u2019s contribution to posterior inference in the presence of prior-likelihood discordance, a broader concept than the usual notion of prior-likelihood conflict. We achieve this dual purpose by extending the classic notion of prior sample size, M, in three directions: (I) estimating M beyond conjugate families; (II) formulating M as a relative notion that is as a function of the likelihood sample size k, M(k), which also leads naturally to a graphical diagnosis; and (III) permitting negative M, as a measure of prior-likelihood conflict, that is, harmful discordance. Our asymptotic regime permits the prior sample size to grow with the likelihood data size, hence making asymptotic arguments meaningful for investigating the impact of the prior relative to that of likelihood. It leads to a simple asymptotic formula for quantifying the impact of a proper prior that only involves computing a centrality and a spread measure of the prior and the posterior. We use simulated and real data to illustrate the potential of the proposed framework, including quantifying how weak is a \u2018weakly informative\u2019 prior adopted in a study of lupus nephritis. Whereas we take a pragmatic perspective in assessing the impact of a prior on a given inference problem under a specific evaluative metric, we also touch upon conceptual and theoretical issues such as using improper priors and permitting priors with asymptotically non-vanishing influence.<\/jats:p>","DOI":"10.1111\/rssb.12414","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T09:29:50Z","timestamp":1617614990000},"page":"413-437","source":"Crossref","is-referenced-by-count":14,"title":["Prior Sample Size Extensions for Assessing Prior Impact and Prior-Likelihood Discordance"],"prefix":"10.1093","volume":"83","author":[{"given":"Matthew","family":"Reimherr","sequence":"first","affiliation":[{"name":"The Pennsylvania State University , University Park, PA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Li","family":"Meng","sequence":"additional","affiliation":[{"name":"Harvard University , Cambridge, MA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan L.","family":"Nicolae","sequence":"additional","affiliation":[{"name":"The University of Chicago , Chicago, IL , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"2023022408371494200_","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1214\/16-BA1024","article-title":"Optimal robustness results for some Bayesian procedures and the relationship to prior-data conflict","volume":"12","author":"Al-Labadi","year":"2017","journal-title":"Bayesian Analysis"},{"key":"2023022408371494200_","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1214\/13-BA824","article-title":"Hypothesis assessment and inequalities for Bayes factors and relative belief ratios","volume":"8","author":"Baskurt","year":"2013","journal-title":"Bayesian 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