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Abstract

This chapter introduces Hamiltonian Monte Carlo and describes its normal use in sampling the canonical distribution.

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References

  1. J. Skilling, Nested sampling. AIP Conf. Proc. 735, 395 (2004)

    Article  ADS  MathSciNet  Google Scholar 

  2. J. Skilling, Nested sampling for general Bayesian computation. Bayesian Anal. 1, 833 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  3. S. Duane, A.D. Kennedy, B.J. Pendleton, D. Roweth, Hybrid Monte Carlo. Phys Lett. B 195, 216 (1987)

    Article  ADS  Google Scholar 

  4. E. Fermi, J.R. Pasta, S.M. Ulam, Studies of nonlinear problems. LASL Report LA-1940 (1955)

    Google Scholar 

  5. B.J. Alder, T. Wainwright, Studies in molecular dynamics. I. General method. J. Chem. Phys. 31, 459 (1959)

    Article  ADS  MathSciNet  Google Scholar 

  6. A. Rahman, Correlations in the motion of atoms in liquid argon. Phys. Rev. 136, A405 (1964)

    Article  ADS  Google Scholar 

  7. D. Frenkel, B. Smit, Understanding Molecular Simulation: From Algorithms to Applications, Computational science series (Elsevier Science, 2001)

    Google Scholar 

  8. D. MacKay, Information Theory Inference and Learning Algorithms (Cambridge University Press, 2003)

    Google Scholar 

  9. R.M. Neal, MCMC using Hamiltonian dynamics, Handbook of Markov Chain Monte Carlo (CRC Press, New York, NY, 2011), p. 113

    Google Scholar 

  10. J. Skilling, Bayesian computation in big spaces-nested sampling and Galilean Monte Carlo, in Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, vol. 1443 (AIP Publishing, 2012), p. 145

    Google Scholar 

  11. F. Feroz, J. Skilling, Exploring multi-modal distributions with nested sampling (2013), arXiv:1312.5638

  12. P. Goggans, R.W. Henderson, N. Xiang, Using nested sampling with Galilean Monte Carlo for model comparison problems in acoustics. Proc. Meet. Acoust. 19, 055089 (2013)

    Article  Google Scholar 

Download references

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Correspondence to Robert John Nicholas Baldock .

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Baldock, R.J.N. (2017). Introduction. In: Classical Statistical Mechanics with Nested Sampling. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-66769-0_11

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