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The Sampling Book Project

The Sampling Book Project

Algorithms

Contour stochastic gradient Langevin dynamics
Cyclical SGLD
Pathfinder
Periodic Orbital MCMC
Use Tempered SMC to Improve Exploration of MCMC Methods.
Tuning inner kernel parameters of SMC
Microcanonical Langevin Monte Carlo

Models

Change of Variable in HMC
Gaussian Regression with the Elliptical Slice Sampler
Bayesian Regression With Latent Gaussian Sampler
Sparse regression
Bayesian Logistic Regression
Bayesian Logistic Regression With Latent Gaussian Sampler
MLP classifier
Hierarchical Bayesian Neural Networks
Regime switching Hidden Markov model
Parameter estimation in ODE models with a probabilistic ODE solver

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