Here , we leverage a logistic stick-breaking representation and recent innovations in Pólya-gamma augmentation to reformu-late the << multinomial distribution >> in terms of [[ latent variables ]] with jointly Gaussian likelihoods , enabling us to take advantage of a host of Bayesian inference techniques for Gaussian models with minimal overhead .
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