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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

Part 5 — Kernel methods

Part 6 — Priors

Introduction to priors Statistical properties of priors Informative priors Function-space priors

Part 7 — Identifiability and symmetries

Part 8 — Scalability

Part 9 — Applications

Part 10 — Topical developments

Part 6 — Priors

Chapter 28. Introduction to priors
Chapter 29. Statistical properties of priors
Chapter 30. Informative priors
Chapter 31. Function-space priors

Meta-learning deep kernel Gaussian processes

Introduction to priors