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Handbook of Bayesian Deep Learning

Handbook of Bayesian Deep Learning

Front matter

Part 1 — Sampling methods

Part 2 — Laplace approximations

Introduction to Laplace approximations Linearised Laplace approximations The differential geometric perspective on Laplace Analytic, low-cost functionality from Laplace Model selection with the marginal likelihood

Part 3 — Variational inference

Part 4 — Ensemble methods

Part 5 — Kernel methods

Part 6 — Priors

Part 7 — Identifiability and symmetries

Part 8 — Scalability

Part 9 — Applications

Part 10 — Topical developments

Part 2 — Laplace approximations

Chapter 7. Introduction to Laplace approximations
Chapter 8. Linearised Laplace approximations
Chapter 9. The differential geometric perspective on Laplace
Chapter 10. Analytic, low-cost functionality from Laplace
Chapter 11. Model selection with the marginal likelihood

Discrete sampling

Introduction to Laplace approximations