• Corpus ID: 236493686

Detecting and diagnosing prior and likelihood sensitivity with power-scaling

  title={Detecting and diagnosing prior and likelihood sensitivity with power-scaling},
  author={Noa Kallioinen and Topi Paananen and Paul-Christian Burkner and Aki Vehtari},
Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationally efficient sensitivity analysis approach using importance sampling to estimate properties of posteriors resulting from power-scaling the prior or likelihood. On this basis, we suggest a diagnostic that can indicate the presence of prior-data conflict or likelihood noninformativity and discuss limitations to this power… 

Quantification of empirical determinacy: the impact of likelihood weighting on posterior location and spread in Bayesian meta-analysis estimated with JAGS and INLA

This work quantified TED, pEDL and pEDS under different modeling conditions such as model parametrization, the primary outcome, and the prior, and clarified to what extent the location and spread of the marginal posterior distributions of the parameters are determined by the data.

Prior knowledge elicitation: The past, present, and future

This work analyzes the state of the art of prior elicitation by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with the expert.

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A call for changing data analysis practices: from philosophy and comprehensive reporting to modeling approaches and back

Many applied disciplines have recognized problems related to the practice of data analysis within their own communities. Some of them have even declared the existence of a statistical crisis that has

Designing translational animal experiments by Bayesian MAP approaches




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Global Robust Bayesian Analysis in Large Models

  • P. Ho
  • Economics
    Federal Reserve Bank of Richmond Working Papers
  • 2019
This paper develops tools for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the framework provides bounds for a wide range of posterior

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The robustness of the assigned prior distribution in a Bayesian estimation problem is examined. A Bayesian analysis for a stochastic intensity parameter of a Poisson distribution is summarized in