Markov-chain Monte Carlo: Some practical implications of theoretical results

Canadian Journal of Statistics

Volumn 26, Issue 1, 1998, Pages 5-20

  Roberts, Gareth O ^a   Rosenthal, Jeffrey S ^a  

^a NONE

Author keywords

Gibbs sampler; Markov chain; MCMC; Metropolis Hastings algorithm; Monte Carlo

Indexed keywords

EID: 0040944354     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.2307/3315667     Document Type: Review

References (4)

1. Geyer, C.J. (1991). Markov chain Monte Carlo maximum likelihood. Computing Science and Statistics: Proc. 23rd Symp. Interface (E.M. Keramidas, ed.), Interface Foundation, Fairfax Station, 156-163.
2. Geyer, C.J., and Thompson, E.A. (1995). Annealing Markov chain Monte Carlo with applications to ancestral inference. J. Amer. Statist. Assoc., 90. 909-920.
3. Marinari, E., and Parisi, G. (1992). Simulated tempering: A new Monte Carlo scheme. Europhys. Lett., 19, 451-458.
4. Tesi, M.C., Janse van Rensburg, E.J., Orlandini, E., and Whittington, S.G. (1996). Interacting self-avoiding walks and polygons in three dimensions. J. Phys. A, 29, 2451-2463.