The paper shows that adding Gaussian noise to a parameter is equivalent to inflating its prior variance, and uses this to claim a Bayesian interpretation of quantization and to justify shrinkage in regularized LDA.
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A Bayesian Framework for Regularized Estimation in Multivariate Models Integrating Approximate Computing Concepts
The paper shows that adding Gaussian noise to a parameter is equivalent to inflating its prior variance, and uses this to claim a Bayesian interpretation of quantization and to justify shrinkage in regularized LDA.