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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

Introduction to variational inference Gaussian mean-field variational inference Dropout-based variational inference Natural-gradient-based variational inference Particle-Based Variational Inference Variational inference in function space Sequential 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 3 — Variational inference

Chapter 12. Introduction to variational inference
Chapter 13. Gaussian mean-field variational inference
Chapter 14. Dropout-based variational inference
Chapter 15. Natural-gradient-based variational inference
Chapter 16. Particle-Based Variational Inference
Chapter 17. Variational inference in function space
Chapter 18. Sequential variational inference

Model selection with the marginal likelihood

Introduction to variational inference