Corpus ID: 190001999

Monte Carlo simulation on the Stiefel manifold via polar expansion

@inproceedings{Jauch2019MonteCS,
  title={Monte Carlo simulation on the Stiefel manifold via polar expansion},
  author={Michael Jauch and Peter L De Hoff and David B. Dunson},
  year={2019}
}
  • Michael Jauch, Peter L De Hoff, David B. Dunson
  • Published 2019
  • Mathematics
  • Motivated by applications to Bayesian inference for statistical models with orthogonal matrix parameters, we present $\textit{polar expansion},$ a general approach to Monte Carlo simulation from probability distributions on the Stiefel manifold. To bypass many of the well-established challenges of simulating from the distribution of a random orthogonal matrix $\boldsymbol{Q},$ we construct a distribution for an unconstrained random matrix $\boldsymbol{X}$ such that $\boldsymbol{Q}_X,$ the… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES

    mcmcse: Monte Carlo Standard Errors for MCMC

    • J. M. Flegal, J. Hughes, D. Vats, N. Dai
    • R package version 1.3-2.
    • 2017
    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Geodesic Monte Carlo on Embedded Manifolds

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Statistics on special manifolds

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    MCMC Using Hamiltonian Dynamics

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Functional Data Analysis

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Stan Development Team (2019)

    • North-HollandNew York
    • Stan Modeling Language Users Guide and Reference Man-
    • 2019