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Volumn 118, Issue 12, 2008, Pages 2198-2222

Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234

Author keywords

Generator; Langevin diffusion; Markov chain Monte Carlo; Optimal scaling; Weak convergence

Indexed keywords

ASYMPTOTIC ANALYSIS; IMAGE SEGMENTATION; SEMICONDUCTOR DOPING;

EID: 55649115998     PISSN: 03044149     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.spa.2007.12.005     Document Type: Article
Times cited : (75)

References (15)
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    • Weak convergence of Metropolis algorithms for non-iid target distributions
    • Bédard M. Weak convergence of Metropolis algorithms for non-iid target distributions. Ann. Appl. Probab. 17 (2007) 1222-1244
    • (2007) Ann. Appl. Probab. , vol.17 , pp. 1222-1244
    • Bédard, M.1
  • 2
    • 55649094261 scopus 로고    scopus 로고
    • M. Bédard, Efficient sampling using Metropolis algorithms: Applications of optimal scaling results, J. Comput. Graph. Statist. (2006) (in press)
    • M. Bédard, Efficient sampling using Metropolis algorithms: Applications of optimal scaling results, J. Comput. Graph. Statist. (2006) (in press)
  • 5
    • 0041906743 scopus 로고    scopus 로고
    • From metropolis to diffusions: Gibbs states and optimal scaling
    • Breyer L.A., and Roberts G.O. From metropolis to diffusions: Gibbs states and optimal scaling. Stochastic Process. Appl. 90 (2000) 181-206
    • (2000) Stochastic Process. Appl. , vol.90 , pp. 181-206
    • Breyer, L.A.1    Roberts, G.O.2
  • 8
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chains and their applications
    • Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 10
    • 33746863011 scopus 로고    scopus 로고
    • Optimal scaling for partially updating MCMC algorithms
    • Neal P., and Roberts G.O. Optimal scaling for partially updating MCMC algorithms. Ann. Appl. Probab. 16 (2007) 475-515
    • (2007) Ann. Appl. Probab. , vol.16 , pp. 475-515
    • Neal, P.1    Roberts, G.O.2
  • 11
    • 0015730787 scopus 로고
    • Optimum Monte-Carlo sampling using Markov chains
    • Peskun P.H. Optimum Monte-Carlo sampling using Markov chains. Biometrika 60 (1973) 607-612
    • (1973) Biometrika , vol.60 , pp. 607-612
    • Peskun, P.H.1
  • 12
    • 0031285157 scopus 로고    scopus 로고
    • Weak convergence and optimal scaling of random walk Metropolis algorithms
    • Roberts G.O., Gelman A., and Gilks W.R. Weak convergence and optimal scaling of random walk Metropolis algorithms. Ann. Appl. Probab. 7 (1997) 110-120
    • (1997) Ann. Appl. Probab. , vol.7 , pp. 110-120
    • Roberts, G.O.1    Gelman, A.2    Gilks, W.R.3
  • 13
    • 0013037129 scopus 로고    scopus 로고
    • Optimal scaling for various Metropolis-Hastings algorithms
    • Roberts G.O., and Rosenthal J.S. Optimal scaling for various Metropolis-Hastings algorithms. Statist. Sci. 16 (2001) 351-367
    • (2001) Statist. Sci. , vol.16 , pp. 351-367
    • Roberts, G.O.1    Rosenthal, J.S.2
  • 14
    • 84890736685 scopus 로고    scopus 로고
    • General state space markov chains and MCMC algorithms
    • Roberts G.O., and Rosenthal J.S. General state space markov chains and MCMC algorithms. Probab. Surveys 1 (2004) 20-71
    • (2004) Probab. Surveys , vol.1 , pp. 20-71
    • Roberts, G.O.1    Rosenthal, J.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.