Truncation to enforce positive-definiteness in separable priors for matrices distorts interpretability and biases sparse posterior inference unless off-diagonal variances are scaled with dimension.
Bayesian computation for high-dimensional gaussian graphical models with spike-and-slab priors.arXiv, 2511.01875:1–139
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Positive-definiteness in separable priors: effects on prior interpretability and inference
Truncation to enforce positive-definiteness in separable priors for matrices distorts interpretability and biases sparse posterior inference unless off-diagonal variances are scaled with dimension.