Exponential Integration for Hamiltonian Monte Carlo

By Wei-Lun Chao, Justin M. Solomon, Dominik Ludewig Michels, Fei Sha. 2015

21 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

1. Jascha Narain Sohl-Dickstein
2. Matthew D. Hoffman
3. Daniel Lévy
4. Michael Robert DeWeese
5. Mayur Mudigonda
6. A. Berger
7. Stefano Ermon
8. Yang Song

Citations in the corpus

• Generalizing Hamiltonian Monte Carlo with Neural Networks (2017, methodology): “…dynamics (Tripuraneni et al., 2016); with variants of AIS adapted to HMC proposals (Sohl-Dickstein & Culpepper, 2012); with the extension of HMC to discrete state spaces (Zhang et al., 2012); and with the use of alternative Hamiltonian integrators (Creutz & Gocksch, 1989; Chao et al., 2015).”
• A Markov Jump Process for More Efficient Hamiltonian Monte Carlo (2015, methodology): “tion techniques [16], Hamiltonian annealed importance sampling [17], split HMC [18], tempered trajectories [9], novel discrete time transition rules [10, 19, 20], stochastic gradient variants on HMC [21], HMC for approximate Bayesian computation [22], and new approximate Hamiltonian integrators [23]”