Optimal Proposal Distributions and Adaptive MCMC
By Jeffrey S. Rosenthal. 2011
421 citations recorded by the source corpus.
We review recent work concerning optimal proposal scalings for Metropolis-Hastings MCMC algorithms, and adaptive MCMC algorithms for trying to improve the algorithm on the y.
View Paper: https://www.semanticscholar.org/paper/64e045eb3290e65649ab6c3d532497f4a87b7a99
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- K. Latuszy'nski
- Jeffrey S. Rosenthal
- Gareth O. Roberts
- Peter J. Green
- Charles C. Margossian
- Bruno Jacobs
- Radford M. Neal
- Dennis Fok
Citations in the corpus
- A review of automatic differentiation and its efficient implementation (2018, methodology): “Modelers have also applied derivatives to sample the high-dimensional posteriors of Bayesian models, using Hamiltonian Monte Carlo samplers (Betancourt, 2018a; Neal, 2011) or approximate these posteriors with variational inference (Kucukelbir, Tran, Ranganath, Gelman, & Blei, 2016).”
- Understanding Large-Scale Dynamic Purchase Behavior (2020, methodology): “The complexity of HMC scales with the size of the parameter space and this complexity cannot be simplified using conditional independence assumptions present in hierarchical models.”
- Adaptive Gibbs samplers and related MCMC methods (2011, methodology): “For Metropolis algorithms there are various optimal scaling results [37, 41, 9, 10, 4, 42, 43, 46] which provide “prescriptions” of how to do this, though they typically depend on unknown characteristics of π.”