Improving the acceptance rate of reversible jump MCMC proposals
By Fahimah Al-Awadhi, Merrilee Hurn, Christopher Jennison. 2004
96 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fcc6f4264e27ad48e88f708fb2a3401f026130dc
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
- Andrew Gelman
- Galin L. Jones
- Xiao-Li Meng
- Steve P. Brooks
- Malcolm Sambridge
- K. Gallagher
- Michael Betancourt
- James P. Hobert
In Statistics and Probability
- Xiao-Li Meng
- Andrew Gelman
- Galin L. Jones
- Steve P. Brooks
- Malcolm Sambridge
- K. Gallagher
- Paul-Christian Burkner
- Michael Betancourt
Citations in the corpus
- Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems (2009, background): “Green and Mira, 2001; Al-Awadhi et al., 2002 ), can be used to improve the acceptance rate by modifying the proposal function during an iteration.”
- Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, background): “In a similar vein, Al-Awadhi et al. (2004) also acknowledge that an initial between-model proposal x′ = gk→k′(x, u) may be poor, and seek to adjust the state x′ to a region of higher posterior probability before taking the decision to accept or reject the proposal.”