Differential Evolution Markov Chain with snooker updater and fewer chains

Statistics and Computing

Volumn 18, Issue 4, 2008, Pages 435-446

  Ter Braak, Cajo J F ^a   Vrugt, Jasper A ^b  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b CENTER FOR NONLINEAR STUDIES   (United States)

Author keywords

Adaptive direction sampling; Adaptive Markov chain Monte Carlo; Differential evolution; Evolutionary Monte Carlo; Metropolis algorithm; Parallel computing; Theophylline kinetics

Indexed keywords

EID: 57849128804     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-008-9104-9     Document Type: Article

References (26)

1. B.V. Babu R. Angira 2006 Modified differential evolution (MDE) for optimization of non-linear chemical processes Comput. Chem. Eng. 30 989 1002
2. Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 2nd edn. Chapman & Hall, London (2004)
3. Gilks, W.R., Roberts, G.O.: Strategies for improving MCMC. In: Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (eds.) Markov Chain Monte Carlo in Practice, pp. 89-114. Chapman & Hall, London (1996)
4. W.R. Gilks G.O. Roberts E.I. George 1994 Adaptive direction sampling Statistician 43 179 189
5. H. Haario E. Saksman J. Tamminen 2001 An adaptive Metropolis algorithm Bernoulli 7 223 242
6. H. Haario M. Laine A. Mira E. Saksman 2006 DRAM: Efficient adaptive MCMC Stat. Comput. 16 339 354
7. S.F. Jarner G.O. Roberts 2007 Convergence of heavy-tailed Monte Carlo Markov chain algorithms Scand. J. Stat. 34 781 815
8. F.M. Liang W.H. Wong 2001 Real-parameter evolutionary Monte Carlo with applications to Bayesian mixture models J. Am. Stat. Assoc. 96 653 666
9. J. Liu J.S. Hodges 2003 Posterior bimodality in the balanced one-way random-effects model J. R. Stat. Soc. Ser. B 65 247 255
10. Mengersen, K., Robert, C.P.: IID sampling using self-avoiding population Monte Carlo: the pinball sampler. In: Bernardo, J.M., Bayarri, M.J., Berger, J.O., Dawid, A.P., Heckerman, D., Smith, A.F.M., West, M. (eds.) Bayesian Statistics 7, pp. 277-292. Clarendon, Oxford (2003)
11. Pinheiro, J.C., Bates, D.M.: Mixed-Effects Models in S and S-PLUS. Springer, New York (2000)
12. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution, A Practical Approach to Global Optimization. Springer, Berlin (2005)
13. Robert, C.P., Casella, G.: Monte Carlo Statistical Methods, 2nd edn. Springer, New York (2004)
14. G.O. Roberts W.R. Gilks 1994 Convergence of adaptive direction sampling J. Multivar. Anal. 49 287 298
15. G.O. Roberts J.S. Rosenthal 2001 Optimal scaling for various Metropolis-Hastings algorithms Stat. Sci. 16 351 367
16. Roberts, G.O.: Linking theory and practice of MCMC. In: Green, P.J., Hjort, N.L., Richardson, S. (eds.) Highly Structured Stochastic Systems, pp. 145-166. Oxford University Press, Oxford (2003)
17. G.O. Roberts J.S. Rosenthal 2007 Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms J. Appl. Probab. 44 458 475
18. Roberts, G.O., Rosenthal, J.S.: Examples of adaptive MCMC. Online ms. (2008)
19. Spiegelhalter, D., Thomas, A., Best, N., Lunn, D.: WinBUGS User Manual version 1.4. www.mrc-bsu.cam.ac.uk/bugs (2003)
20. R. Storn K. Price 1997 Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces J. Glob. Optim. 11 341 359
21. Strens, M., Bernhardt, M., Everett, N.: Markov chain Monte Carlo sampling using direct search optimization. In: Sammut, C., Hoffmann, A.G. (eds.) Machine Learning, Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pp. 602-609. Morgan Kaufmann, San Fransisco (2002)
22. C.J.F. ter Braak 2006 A Markov chain Monte Carlo version of the genetic algorithm differential evolution: easy Bayesian computing for real parameter spaces Stat. Comput. 16 239 249
23. Thomas, A., O'Hara, R.B.: OpenBUGS. http://mathstat.helsinki.fi/openbugs/ (2007)
24. Vrugt, J.A., ter Braak, C.J.F., Gupta, H.V., Robinson, B.A.: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment (SERRA) (2008a). DOI: 10.1007/s00477-008-0274-y
25. Vrugt, J.A., ter Braak, C.J.F., Clark, M.P., Hyman, J.M., Robinson, B.A.: Treatment of input uncertainty in hydrologic modeling: doing hydrology backwards with Markov chain Monte Carlo simulation. Water Resour. Res. (2008b, in press)
26. R. Waagepetersen D. Sorensen 2001 A tutorial on reversible jump MCMC with a view toward applications in QTL-mapping Int. Stat. Rev. 69 49 61