1. MCMC
1.1. Methods
1.1.1. HMC
1.1.1.1. Basics
1.1.1.1.1. A Conceptual Introduction to HMC
1.1.1.1.2. MCMC using Hamiltonian Dynamics
1.1.1.2. Tuning and Scaling
1.1.1.2.1. Optimal Scaling for LMC
1.1.1.3. Extensions
1.1.1.3.1. Gradient-Free
1.1.1.3.2. Stochastic Approximation of Gradients
1.1.2. Manifolds
1.1.2.1. Sampling from a Manifold
1.1.3. Random Walk Metropolis
1.1.3.1. Adaptive Proposal
1.1.3.1.1. Adaptive Proposal for RWM
1.1.3.2. Optimality
1.1.3.2.1. Efficient Metropolis Jumping Rules
2. Normalizing Flows
3. GANs
4. ABC
4.1. Methods
4.1.1. Sampling
4.1.1.1. Rejection-ABC
4.1.1.1.1. Original Paper
4.1.1.1.2. Regression-Adjustment
4.1.1.2. IS-ABC
4.1.1.2.1. Resource 1
4.1.1.2.2. Resource 2
4.1.1.3. MCMC-ABC
4.1.1.3.1. Original Paper
4.1.1.3.2. Tolerance-Augmented Space
4.1.1.4. SMC-ABC
4.1.1.4.1. Precursors
4.1.1.4.2. Original Paper - Adaptive SMC-ABC
4.1.1.4.3. Extensions
4.1.2. Variational
4.1.3. Expectation-Propagation
4.2. Components
4.2.1. Summary Statistics
4.2.2. ABC Kernel
4.2.3. Simulator
4.2.3.1. Using Random Seeds
4.2.3.1.1. Efficient likelihood-free Bayesian Computation for household epidemics
4.2.3.1.2. Asymptotically exact inference in differentiable generative models
4.2.3.1.3. Sampling Seeds Afresh
4.2.3.1.4. Sampling Seeds given Parameter
4.3. Tutorials
4.3.1. From Rejection-ABC to MCMC-ABC
4.3.2. The Handbook of ABC