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This document provides an introduction to Bayesian neural networks (BNNs). It discusses that BNNs treat neural network parameters as random variables and perform approximate Bayesian inference over them. This allows BNNs to estimate uncertainty in predictions. The document outlines variational inference (VI) as a common approach for approximate Bayesian inference in BNNs. Specifically, it describes mean-field VI using fully factorized distributions, known as Bayes-by-backprop, where the variational paramete

Author

hu jack

Language

EN