On the geometric ergodicity of hybrid samplers

By Gersende Fort, Eric Moulines, Gareth O. Roberts, Jeffrey S. Rosenthal. Journal of Applied Probability, 2003

57 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Spectroscopy, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/de9fb87ad17c599b9f44504d97f00e9dfd8e03ec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Antonietta Mira
2. Theodore Papamarkou
3. Adrien Saumard
4. Jon A. Wellner
5. K. Latuszy'nski
6. Galin L. Jones
7. Jeffrey S. Rosenthal
8. Gareth O. Roberts

In Spectroscopy

1. K. Latuszy'nski
2. Jeffrey S. Rosenthal
3. Galin L. Jones
4. Gareth O. Roberts
5. Mark A. Girolami
6. Antonietta Mira
7. Radford M. Neal
8. Carl John Scarrott

In Statistics and Probability

1. Mark A. Girolami
2. K. Latuszy'nski
3. Theodore Papamarkou
4. Galin L. Jones
5. Gareth O. Roberts
6. Jeffrey S. Rosenthal
7. Antonietta Mira
8. Chris J. Oates

Citations in the corpus

• Zero Variance Differential Geometric Markov Chain Monte Carlo Algorithms (2014, methodology): “…MCMC include Rao-Blackwellization (Gelfand and Smith (1990), Robert and Casella (2004)), hybrid Monte Carlo (Duane et al. (1987), Fort et al. (2003)), use of Riemann sums (Philippe and Robert (2001)) or of auxiliary variables (Swendsen andWang (1987), Mira and Tierney…”
• Zero variance Markov chain Monte Carlo for Bayesian estimators (2010, background): “These modifications have been achieved in many different ways, for example by trying to induce negative correlation along the chain path ([6,21,13,41,12]); by trying to avoid random walk behavior via successive over-relaxation ([1,36,7]); by hybrid Monte Carlo ([16,35,10,18,27]); by exploiting non r”
• Log-Concavity and Strong Log-Concavity: a review (2014, background): “For entry points to this literature, see Gilks and Wild (1992), Polson (1996), Brooks (1998), Roberts and Rosenthal (2002), Fort et al. (2003), Jylänki, Vanhatalo and Vehtari (2011), and Rudolf (2012).”
• Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition (2009, background): “In particular, there has been almost none in the case where the component-wise updates are done with Metropolis–Hastings (but see Fort et al., 2003; Jones, Roberts and Rosenthal, 2013; Roberts and Rosenthal, 1998).”
• Adaptive Gibbs samplers and related MCMC methods (2011, methodology): “The proof is along the same lines as the proof of Theorem 5.5 and the proof of Theorem 3 of [17] and is omitted.”