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Handbook of Bayesian Deep Learning

Handbook of Bayesian Deep Learning

Front matter

Part 1 — Sampling methods

Part 2 — Laplace approximations

Part 3 — Variational inference

Part 4 — Ensemble methods

Introduction and connections with approximate Bayesian inference Hybrid ensemble sampling methods Practical applications of deep ensembles PAC-Bayesian deep neural network ensembles

Part 5 — Kernel methods

Part 6 — Priors

Part 7 — Identifiability and symmetries

Part 8 — Scalability

Part 9 — Applications

Part 10 — Topical developments

Introduction and connections with approximate Bayesian inference

Chapter migration pending.

Part 4 — Ensemble methods

Hybrid ensemble sampling methods