Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Paper summary This paper proposes a way to speed up Hamiltonian Monte Carlo (HMC) \cite{Duane1987216} sampling for hierarchical models. It is similar in spirit to RMHMC, in which the mass matrix varies according to local topology, except that here the mass matrices for each parameter type (parameter or hyperparameter) only depend on their counterpart, which allows an explicit leapfrog integrator to be used to simulate dynamics rather than an implicit integrator requiring fixed-point iteration to convergence for each step. The authors point out that their method goes beyond straightforward Gibbs sampling with HMC within each Gibbs step since their method leaves the counterpart parameter's momentum intact.
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Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Zhang, Yichuan and Sutton, Charles A.
Neural Information Processing Systems Conference - 2014 via Bibsonomy
Keywords: dblp


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