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Handbook of Bayesian Deep Learning

Handbook of Bayesian Deep Learning

Front matter

Part 1 — Sampling methods

Introduction to sampling Stochastic gradient MCMC Simulation-based inference Sequential Monte Carlo samplers Low-precision sampling Discrete sampling

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

Part 1 — Sampling methods

Chapter 1. Introduction to sampling
Chapter 2. Stochastic gradient MCMC
Chapter 3. Simulation-based inference
Chapter 4. Sequential Monte Carlo samplers
Chapter 5. Low-precision sampling
Chapter 6. Discrete sampling

Introduction to sampling