PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

NeurIPS 2017 Jonathan H. HugginsRyan P. AdamsTamara Broderick

Generalized linear models (GLMs) -- such as logistic regression, Poisson regression, and robust regression -- provide interpretable models for diverse data types. Probabilistic approaches, particularly Bayesian ones, allow coherent estimates of uncertainty, incorporation of prior information, and sharing of power across experiments via hierarchical models... (read more)

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