• Mathematics
  • Published 2014

Optimizing The Integrator Step Size for Hamiltonian Monte Carlo

@inproceedings{Betancourt2014OptimizingTI,
  title={Optimizing The Integrator Step Size for Hamiltonian Monte Carlo},
  author={M. J. R{\'i}os Betancourt and Simon Byrne and Mark Girolami},
  year={2014}
}
Hamiltonian Monte Carlo can provide powerful inference in complex statistical problems, but ultimately its performance is sensitive to various tuning parameters. In this paper we use the underlying geometry of Hamiltonian Monte Carlo to construct a universal optimization criteria for tuning the step size of the symplectic integrator crucial to any implementation of the algorithm as well as diagnostics to monitor for any signs of invalidity. An immediate outcome of this result is that the… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 16 REFERENCES

Optimal Tuning

A. Beskos, N. Pillai, G. Roberts, J. Sanz-Serna, S. Andrew
  • 2013
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Statistical science 457–472

E. Hairer, C. Lubich, G. Wanner
  • 2006

On the Non

M. Perlmutter, G.R.W. Quispel
  • 2004
VIEW 1 EXCERPT

Slice Sampling

R. M. Neal
  • Annals of Statistics
  • 2003