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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

Part 7 — Identifiability and symmetries

Part 8 — Scalability

Part 9 — Applications

Part 10 — Topical developments

Subspace inference Implicitly Bayesian prediction rules Diffusion models Prior-data Fitted Networks Advances in singular learning theory for BDL Active Learning and Experimental Design Causal-aware BDL From Credal Bayesian Deep Learning to Scalable Evidential Classification Bayesian Reinforcement Learning

Part 10 — Topical developments

Chapter 48. Subspace inference
Chapter 49. Implicitly Bayesian prediction rules
Chapter 50. Diffusion models
Chapter 51. Prior-data Fitted Networks
Chapter 52. Advances in singular learning theory for BDL
Chapter 53. Active Learning and Experimental Design
Chapter 54. Causal-aware BDL
Chapter 55. From Credal Bayesian Deep Learning to Scalable Evidential Classification
Chapter 56. Bayesian Reinforcement Learning

Software for BDL

Subspace inference