Kernel Adaptive Metropolis-Hastings

By Dino Sejdinovic, Heiko Strathmann, M. Garcia, Christophe Andrieu, Arthur Gretton. 2013

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

1. Matthew D. Hoffman
2. Jascha Narain Sohl-Dickstein
3. Daniel Lévy
4. Stefano Ermon
5. David M. Blei
6. Yang Song
7. Diederik P. Kingma
8. Jun Zhu

Citations in the corpus

• Generalizing Hamiltonian Monte Carlo with Neural Networks (2017, methodology): “Kernel methods have been used both for learning a proposal distribution (Sejdinovic et al., 2014) and for approximating the gradient of the energy (Strathmann et al.”