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- W4387221063 abstract "Bayesian analysis methods provide a significant advancement in network psychometrics, allowing researchers to use the edge inclusion Bayes factorfor testing conditional independence between pairs of variables in the network. Using this methodology requires prior distributions on the network parameters and on the network’s structure. However, the impact of both prior distributions on the inclusion Bayes factor is unexplored. In this paper, we focus on a specific class of Markov Random Field models for ordinal and binary data and first discuss the different choices of prior distributions for the network parameters and the network structure, and then use an extensive simulation study to assess the sensitivity of the models to thesedistributions. An interactive Shiny app makes the simulation results easily accessible. Based on our results, we provide practical recommendations forapplied researchers." @default.
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- W4387221063 date "2023-09-29" @default.
- W4387221063 modified "2023-10-01" @default.
- W4387221063 title "Sensitivity Analysis of Prior Distributions in Bayesian Graphical Modeling: Guiding Informed Prior Choices for Conditional Independence Testing" @default.
- W4387221063 doi "https://doi.org/10.31234/osf.io/6m7ca" @default.
- W4387221063 hasPublicationYear "2023" @default.
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