A Conceptual Introduction to Hamiltonian Monte Carlo

[author] Betancourt, MichaelM. (2013b). Generalizing the No-U-Turn Sampler to Riemannian Manifolds. ArXiv e-prints 1304.1920

1920

Earlier work this paper cites.

[author] Metropolis, NicholasN., Rosenbluth, Arianna WA. W., Rosenbluth, Marshall NM. N., Teller, Augusta HA. H. and Teller, EdwardE. (1953). Equation of State Calculations by Fast Computing Machines. The journal of chemical physics 21 1087

1953

Earlier work this paper cites.

[author] Hastings, W KeithW. K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika 57 97–109

1970

Earlier work this paper cites.

[author] Duane, SimonS., Kennedy, A. D.A. D., Pendleton, Brian J.B. J. and Roweth, DuncanD. (1987). Hybrid Monte Carlo. Physics Letters B 195 216 - 222

1987

Earlier work this paper cites.

[author] Geyer, Charles JC. J. (1992). Practical Markov Chain Monte Carlo. Statistical Science 473–483

1992

Earlier work this paper cites.

[author] Neal, Radford MR. M. (1994). An Improved Acceptance Procedure for the Hybrid Monte Carlo Algorithm. Journal of Computational Physics 111 194–203

1994

Earlier work this paper cites.

[author] Neal, Radford MR. M. (1995). Bayesian Learning for Neural Networks PhD thesis, University of Toronto

1995

Earlier work this paper cites.

[author] Robert, Christian PC. P. and Casella, GeorgeG. (1999). Monte Carlo Statistical Methods. Springer New York

1999

Earlier work this paper cites.

[author] MacKay, David J. C.D. J. C. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press, New York

2003

Earlier work this paper cites.

[author] Leimkuhler, B.B. and Reich, S.S. (2004). Simulating Hamiltonian Dynamics. Cambridge University Press, New York

2004

Earlier work this paper cites.

[author] Roberts, Gareth OG. O. and Rosenthal, Jeffrey SJ. S. (2004). General State Space Markov Chains and MCMC Algorithms. Probability Surveys 1 20–71

2004

Cited alongside, same era.

[author] Bishop, Christopher M.C. M. (2006). Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York

2006

Cited alongside, same era.

[author] Hairer, E.E., Lubich, C.C. and Wanner, G.G. (2006). Geometric Numerical Integration: Structure-Preserving Algorithms for Ordinary Differential Equations. Springer, New York

2006

Cited alongside, same era.

[author] Brooks, SteveS., Gelman, AndrewA., Jones, Galin L.G. L. and Meng, Xiao-LiX.-L., eds. (2011). Handbook of Markov Chain Monte Carlo. CRC Press, New York

2011

Cited alongside, same era.

[author] Girolami, MarkM. and Calderhead, BenB. (2011). Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73 123–214

[author] Blanes, SergioS., Casas, FernandoF. and Sanz-Serna, J. M.J. M. (2014). Numerical integrators for the Hybrid Monte Carlo method. ArXiv e-prints 1405.3153

2014

Later among the works it cites.

[author] Gelman, AndrewA., Carlin, John B.J. B., Stern, Hal S.H. S., Dunson, David B.D. B., Vehtari, AkiA. and Rubin, Donald B.D. B. (2014). Bayesian Data Analysis, third ed. Texts in Statistical Science Series. CRC Press, Boca Raton, FL

2014

Later among the works it cites.

[author] Hoffman, Matthew D.M. D. and Gelman, AndrewA. (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15 1593–1623

2014

Later among the works it cites.

[author] Holmes, SusanS., Rubinstein-Salzedo, SimonS. and Seiler, ChristofC. (2014). Curvature and Concentration of Hamiltonian Monte Carlo in High Dimensions

2014

Later among the works it cites.