rstiefel

rstiefel: Random orthonormal matrix generation on the Stiefel manifold. This package simulates random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) ”Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data.”


References in zbMATH (referenced in 26 articles )

Showing results 1 to 20 of 26.
Sorted by year (citations)

1 2 next

  1. Mantoux, Clément; Durrleman, Stanley; Allassonnière, Stéphanie: Asymptotic analysis of a matrix latent decomposition model (2022)
  2. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Monte Carlo simulation on the Stiefel manifold via polar expansion (2021)
  3. Pourzanjani, Arya A.; Jiang, Richard M.; Mitchell, Brian; Atzberger, Paul J.; Petzold, Linda R.: Bayesian inference over the Stiefel manifold via the Givens representation (2021)
  4. Subedi, Sanjeena; McNicholas, Paul D.: A variational approximations-DIC rubric for parameter estimation and mixture model selection within a family setting (2021)
  5. Ando, Tomohiro; Bai, Jushan: Quantile co-movement in financial markets: a panel quantile model with unobserved heterogeneity (2020)
  6. Berger, James O.; Sun, Dongchu; Song, Chengyuan: Bayesian analysis of the covariance matrix of a multivariate normal distribution with a new class of priors (2020)
  7. Berger, James O.; Sun, Dongchu; Song, Chengyuan: An objective prior for hyperparameters in normal hierarchical models (2020)
  8. Jauch, Michael; Hoff, Peter D.; Dunson, David B.: Random orthogonal matrices and the Cayley transform (2020)
  9. Pal, Subhadip; Sengupta, Subhajit; Mitra, Riten; Banerjee, Arunava: Conjugate priors and posterior inference for the matrix Langevin distribution on the Stiefel manifold (2020)
  10. Park, Jong Hee; Sohn, Yunkyu: Detecting structural changes in longitudinal network data (2020)
  11. Franks, Alexander M.; Hoff, Peter: Shared subspace models for multi-group covariance estimation (2019)
  12. Duan, Leo L.; Johndrow, James E.; Dunson, David B.: Scaling up data augmentation MCMC via calibration (2018)
  13. Kent, John T.; Ganeiber, Asaad M.; Mardia, Kanti V.: A new unified approach for the simulation of a wide class of directional distributions (2018)
  14. Tsilifis, P.; Ghanem, R. G.: Bayesian adaptation of chaos representations using variational inference and sampling on geodesics (2018)
  15. Marowka, Maciej; Peters, Gareth W.; Kantas, Nikolas; Bagnarosa, Guillaume: Some recent developments in Markov chain Monte Carlo for cointegrated time series (2017)
  16. van Berkum, Frank; Antonio, Katrien; Vellekoop, Michel: A Bayesian joint model for population and portfolio-specific mortality (2017)
  17. Chowdhary, Kenny; Najm, Habib N.: Bayesian estimation of Karhunen-Loève expansions; a random subspace approach (2016)
  18. Verbanck, Marie; Josse, Julie; Husson, François: Regularised PCA to denoise and visualise data (2015)
  19. Josse, Julie; van Eeuwijk, Fred; Piepho, Hans-Peter; Denis, Jean-Baptiste: Another look at Bayesian analysis of AMMI models for genotype-environment data (2014)
  20. Kurt Hornik; Bettina Grün: movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions (2014) not zbMATH

1 2 next