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All issues Volume 55 (2013) EPJ Web of Conferences, 55 (2013) 02002 References
Open Access
Issue
EPJ Web of Conferences
Volume 55, 2013
SOS 2012 – IN2P3 School of Statistics
Article Number 02002
Number of page(s) 21
Section Multivariate Analysis Tools
DOI https://doi.org/10.1051/epjconf/20135502002
Published online 01 July 2013
  1. C. Andrieu, E. Moulines, and P. Priouret. Stability of stochastic approximation under verifiable conditions. SIAM Journal on Control and Optimization, 44:283–312, 2005. [Google Scholar]
  2. C. Andrieu and J. Thoms. A tutorial on adaptive MCMC. Statistics and Computing, 18:343–373, 2008. [Google Scholar]
  3. Christophe Andrieu, Arnaud Doucet, and Roman Holenstein. Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society B, 2010. [Google Scholar]
  4. Y. Atchadé, G. Fort, E. Moulines, and P. Priouret. Bayesian Time Series Models, chapter Adaptive Markov chain Monte Carlo: Theory and Methods, pages 33–53. Cambridge Univ. Press, 2011. [Google Scholar]
  5. R. Bardenet. Towards adaptive learning and inference – Applications to hyperparameter tuning and astroparticle physics. PhD thesis, Université Paris-Sud, 2012. [Google Scholar]
  6. R. Bardenet and B. Kégl. An adaptive Monte Carlo Markov chain algorithm for inference from mixture signals. In Proceedings of ACAT’11, Journal of Physics: Conference series, 2012. [Google Scholar]
  7. A. Doucet, N. de Freitas, and N. Gordon. Sequential Monte Carlo in practice. Springer, 2001. [Google Scholar]
  8. H. J. Drescher, M. Hladik, S. Ostapchenko, T. Pierog, and Werner K. Parton-based Gribov-Regge theory. Physics Reports, 2001. [Google Scholar]
  9. P. Fearnhead and D. Prangle. Constructing summary statistics for approximate Bayesian computation: semi-automatic ABC. Journal of the Royal Statistical Society B, 2012. [Google Scholar]
  10. J. M. Flegal and G. L. Jones. Batch means and spectral variance estimators in Markov chain Monte Carlo. Annals of Statistics, 38(2):1034–1070, 2010. [CrossRef] [Google Scholar]
  11. W.R. Gilks, S. Richardson, and D. Spiegelhalter, editors. Markov Chain Monte Carlo in Practice. Chapman & Hall, 1996. [Google Scholar]
  12. P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711–732, 1995. [Google Scholar]
  13. G. R. Grimmett and D. R. Stirzaker. Probability and random processes. Oxford science publications, second edition, 1992. [Google Scholar]
  14. H. Haario, E. Saksman, and J. Tamminen. An adaptive Metropolis algorithm. Bernoulli, 7:223–242, 2001. [CrossRef] [MathSciNet] [Google Scholar]
  15. D. Heck, J. Knapp, J. N. Capdevielle, G. Schatz, and T. Thouw. CORSIKA: A Monte Carlo code to simulate extensive air showers. Technical report, Forschungszentrum Karlsruhe, 1998. [Google Scholar]
  16. S. F. Jarner and E. Hansen. Geometric ergodicity of Metropolis algorithms. Stochastic processes and their applications, 341–361, 1998. [Google Scholar]
  17. R. M. Neal. Handbook of Markov Chain Monte Carlo, chapter MCMC using Hamiltonian dynamics. Chapman & Hall / CRC Press, 2010. [Google Scholar]
  18. C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer-Verlag, New York, 2004. [CrossRef] [Google Scholar]
  19. G. Roberts, A. Gelman, and W. Gilks. Weak convergence of optimal scaling of random walk Metropolis algorithms. The Annals of Applied Probability, 7:110–120, 1997. [Google Scholar]
  20. G. O. Roberts and J. S. Rosenthal. Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science, 16:351–367, 2001. [Google Scholar]
  21. A. Roodaki. Signal decompositions using trans-dimensional Bayesian methods. PhD thesis, Supélec, 2012. [Google Scholar]
  22. D. S. Sivia and J. Skilling. Data Analysis: A Bayesian Tutorial. Oxford University press, second edition, 2006. [Google Scholar]
  23. A.D. Sokal. Monte Carlo methods in statistical mechanics: Foundations and new algorithms, 1996. Lecture notes at the Cargèse summer school. [Google Scholar]
  24. D. Wraith, M. Kilbinger, K. Benabed, O. Cappé, J.-F. Cardoso, G. Fort, S. Prunet, and C. P. Robert. Estimation of cosmological parameters using adaptive importance sampling. Phys. Rev. D, 80(2), 2009. [Google Scholar]

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