History Background: for the task of simulating the distribution of states for a system of idealized molecules (Metropolis, et al., 1953 MCMC with Metropolis test (Alder Wainwright, 1959 ): deterministic approach by simulating Hamiltonian dynamics Birth: combining(hybrid) (Duane, et al., 1987): Hybrid Monte Carlo. Renamed as Hamiltonian monte carlo afterwards Application to statistics (Neal, 1993ab probabilistic inference and Bayesian learning (Neal, 1996a]: neural network models (Ishwaran, 1999 ): generalized linear modelsHistory • Background: for the task of simulating the distribution of states for a system of idealized molecules, – (Metropolis, et al., 1953): MCMC with Metropolis test. – (Alder & Wainwright, 1959): deterministic approach by simulating Hamiltonian dynamics. • Birth: combining (hybrid) – (Duane, et al., 1987): Hybrid Monte Carlo. Renamed as Hamiltonian Monte Carlo afterwards. • Application to statistics – (Neal, 1993ab): probabilistic inference and Bayesian learning – (Neal, 1996a): neural network models – (Ishwaran, 1999): generalized linear models – …