Riemannian Manifold Hamiltonian Monte Carlo

@inproceedings{Girolami2009RiemannianMH,
  title={Riemannian Manifold Hamiltonian Monte Carlo},
  author={Mark A Girolami},
  year={2009}
}
The paper proposes a Riemannian Manifold Hamiltonian Monte Carlo sampler to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The method provides a fully automated adaptation mechanism that circumvents the costly pilot runs required to tune proposal densities for Metropolis-Hastings or indeed Hybrid Monte Carlo and Metropolis Adjusted Langevin Algorithms. This allows for highly efficient… CONTINUE READING
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Showing 1-10 of 14 references

1996).Bayesian Learing for Neural Networks

  • R. M. Neal
  • 1996
Highly Influential
15 Excerpts

1990).Differential-Geometrical Methods in Statistics

  • S. Amari
  • 1990
Highly Influential
4 Excerpts

The Geometry of Asymptotic Inference

  • R. E. Kass
  • 1989
Highly Influential
4 Excerpts

Efficient Cosmological Parameter Estimat

  • A. Hajian
  • 2007
2 Excerpts

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