Optimal scaling of Metropolis algorithms: Heading toward general target distributions

By Mylene B'edard, Jeffrey S. Rosenthal. 2008

71 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4a3b7f696ccc013e45f6ea031a6b120e369dfd60

How researchers see this paper

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

In Artificial Intelligence

1. R. Neath
2. Radford M. Neal
3. Galin L. Jones
4. Alicia A. Johnson
5. T. Griffiths
6. Michael I. Jordan
7. Athanasios Kottas
8. Ruslan Salakhutdinov

In Statistics and Probability

1. Radford M. Neal
2. R. Neath
3. Galin L. Jones
4. Alicia A. Johnson
5. David M. Blei
6. T. Griffiths
7. Zoubin Ghahramani
8. Ying Nian Wu

Citations in the corpus

• Optimal Proposal Distributions and Adaptive MCMC (2008, background): “(5) In a related direction, Bédard (2007, 2008, 2006; see also Bédard and Rosenthal, 2008) considered the case where the target distribution π has independent coordinates with vastly different scalings (i.e., different powers of d as d → ∞).”
• Component-Wise Markov Chain Monte Carlo: Uniform and Geometric Ergodicity under Mixing and Composition (2009, methodology): “This has led to investigation of optimal scaling of Metropolis algorithms and so-called adaptive algorithms which allow the proposal kernel to change over the course of the simulation (see, e.g., Bédard and Rosenthal, 2008; Rosenthal, 2011).”