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Increases in the use of Bayesian inference in applied analysis, the complexity of estimated models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under conjugate priors have led to more scrutiny regarding the specification of the parameters in prior distributions. Impact of prior parameter assumptions on posterior statistics is commonly investigated in terms of local or pointwise assessments, in the form of derivatives or more often multiple evaluations under a set of alternative prior parameter specifications. This paper expands upon these localized strategies and introduces a new approach based on the graph of posterior statistics over prior parameter regions (sensitivity manifolds) that offers additional measures and graphical assessments of prior parameter dependence. Estimation is based on multiple point evaluations with Gaussian processes, with efficient selection of evaluation points via active learning, and is further complemented with derivative information. The application introduces a strategy to assess prior parameter dependence in a multivariate demand model with a high dimensional prior parameter space, where complex prior-posterior dependence arises from model parameter constraints. The new measures uncover a considerable prior dependence beyond parameters suggested by theory, and reveal novel interactions between the prior parameters and the elasticities. [ABSTRACT FROM AUTHOR]

In this paper, a new class of composite model is proposed for modeling actuarial claims data of mixed sizes. The model is developed using the Stoppa distribution and a mode-matching procedure. The use of the Stoppa distribution allows for more flexibility over the thickness of the tail, and the mode-matching procedure gives a simple derivation of the model compositing with a variety of distributions. In particular, the Weibull–Stoppa and the Lognormal–Stoppa distributions are investigated. Their performance is compared with existing composite models in the context of the well-known Danish fire insurance data-set. The results suggest the composite Weibull–Stoppa model outperforms the existing composite models in all seven goodness-of-fit measures considered. [ABSTRACT FROM PUBLISHER]

Supplemental material is available for this article. Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue., Competing Interests: Disclosures of conflicts of interest: H.M.L.F. Financial grant awarded by Australian Government Medical Research Future Fund (MRFF) funded development, in-kind support provided by the five partners (BreastScreen Victoria [BSV], St Vincent's Hospital Melbourne [SVHM], St Vincent's Institute of Medical Research [SVI], University of Melbourne [UOM], University of Adelaide [UOA]); honorary member of AI Advisory Committee for Royal Australian and New Zealand College of Radiologists. J.S.N.T. Employee of annalise.ai. M.S.E. No relevant relationships. K.M.K. No relevant relationships. B.H. Employed at SVI during 2021 and performed data engineering work for the results in this manuscript. R.K. No relevant relationships. C.F.K. No relevant relationships. C.A.P.S. No relevant relationships. Y.C. No relevant relationships. C.W. No relevant relationships. O.A.Q. No relevant relationships. S.K.F. No relevant relationships. S.L. No relevant relationships. E.M. No relevant relationships. T.L.N. No relevant relationships. D.F.S. National Breast Cancer Foundation Grant IIRS-18-093. P.B.R. No relevant relationships. J.F.L. No relevant relationships. P.B. Employee of annalise.ai. J.L.H. No relevant relationships. G.C. Employment at the UOA; project funded by MRFF grant; support for attending meetings and/or travel from MRFF grant; two patent applications. D.J.M. This manuscript arose from work supported by a grant awarded by the MRFF of Australia (no. MRFAI000090: BRAIx), grant payments made to author's institution and SVI and partially distributed to other partner institutions (SVHM, UOM, UOA, BSV); this work was also supported by a Ramaciotti Health Investment Grant from the Ramaciotti Foundation in Australia (Faster, cheaper, more accurate breast cancer screening: training computers to detect disease)., (© 2023 by the Radiological Society of North America, Inc.)