The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics
نویسندگان
چکیده
The prior distribution is a crucial building block in Bayesian analysis, and its choice will impact the subsequent inference. It therefore important to have convenient way quantify this impact, as such measure of help choose between two or more priors given situation. To end new approach, Wasserstein Impact Measure (WIM), introduced. In three simulated scenarios, WIM compared competitor measures from literature, versatility illustrated via real datasets.
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چکیده ندارد.
15 صفحه اولEvaluating the Impact of Prior Assumptions in Bayesian Biostatistics.
A common concern in Bayesian data analysis is that an inappropriately informative prior may unduly influence posterior inferences. In the context of Bayesian clinical trial design, well chosen priors are important to ensure that posterior-based decision rules have good frequentist properties. However, it is difficult to quantify prior information in all but the most stylized models. This issue ...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2022
ISSN: ['0167-9473', '1872-7352']
DOI: https://doi.org/10.1016/j.csda.2021.107352