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Convergence of Adaptive Direction Sampling

Author

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  • Roberts, G. O.
  • Gilks, W. R.

Abstract

We consider the convergence of adaptive direction sampling, concentrating mainly on a special case, the "snooker algorithm" for which a powerful irreducibility result can be proved under extremely mild regularity conditions.

Suggested Citation

  • Roberts, G. O. & Gilks, W. R., 1994. "Convergence of Adaptive Direction Sampling," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 287-298, May.
    • Handle: RePEc:eee:jmvana:v:49:y:1994:i:2:p:287-298
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    2. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2020. "Revealing forecaster's preferences: A Bayesian multivariate loss function approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 412-437, April.
    3. Bauwens, Luc & Bos, Charles S. & van Dijk, Herman K. & van Oest, Rutger D., 2004. "Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods," Journal of Econometrics, Elsevier, vol. 123(2), pages 201-225, December.
    4. Ricardo S. Ehlers & Stephen P. Brooks, 2008. "Adaptive Proposal Construction for Reversible Jump MCMC," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 677-690, December.
    5. Wang, Zhonglei, 2019. "Markov chain Monte Carlo sampling using a reservoir method," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 64-74.
    6. Tore Selland Kleppe, 2016. "Adaptive Step Size Selection for Hessian-Based Manifold Langevin Samplers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 788-805, September.
    7. Bo Hu & Yuan Ji & Kam-Wah Tsui, 2008. "Bayesian Estimation of Inverse Dose Response," Biometrics, The International Biometric Society, vol. 64(4), pages 1223-1230, December.
    8. Hoogerheide, L.F. & van Dijk, H.K. & van Oest, R.D., 2007. "Simulation based bayesian econometric inference: principles and some recent computational advances," Econometric Institute Research Papers EI 2007-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Ross Corkrey & Tom A McMeekin & John P Bowman & David A Ratkowsky & June Olley & Tom Ross, 2014. "Protein Thermodynamics Can Be Predicted Directly from Biological Growth Rates," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-15, May.
    10. Rigat, F. & Mira, A., 2012. "Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1450-1467.
    11. Takefumi Yamazaki, 2018. "Financial friction sources in emerging economies: Structural estimation of sovereign default models," Discussion papers ron303, Policy Research Institute, Ministry of Finance Japan.
    12. Hu, Bo & Tsui, Kam-Wah, 2010. "Distributed evolutionary Monte Carlo for Bayesian computing," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 688-697, March.
    13. Mbalawata, Isambi S. & Särkkä, Simo & Vihola, Matti & Haario, Heikki, 2015. "Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 101-115.

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