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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

Bayesian LLMs Confidence-based selective classification Biomedical data imputation Deep Bayesian spatio-temporal modelling of wildfires Software for BDL

Part 10 — Topical developments

Part 9 — Applications

Chapter 43. Bayesian LLMs
Chapter 44. Confidence-based selective classification
Chapter 45. Biomedical data imputation
Chapter 46. Deep Bayesian spatio-temporal modelling of wildfires
Chapter 47. Software for BDL

Learned Compression with Stochastic Codes