Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation

DOI10.1080/00949655.2017.1416470OpenAlexW2565819431WikidataQ59765822 ScholiaQ59765822MaRDI QIDQ4960588

Babak Shahbaba, Alexander Vandenberg-Rodes, Shiwei Lan, Andrew J. Holbrook

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1612.08224

zbMATH Keywords

Riemannian geometryspectral analysisHMC

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (14)

Massive Parallelization Boosts Big Bayesian Multidimensional Scaling ⋮ Time-varying spectral matrix estimation via intrinsic wavelet regression for surfaces of Hermitian positive definite matrices ⋮ Nonparametric Bayesian modelling of longitudinally integrated covariance functions on spheres ⋮ A Riemann-Stein kernel method ⋮ Nonparametric Bayesian modeling and estimation of spatial correlation functions for global data ⋮ Some recent developments in Markov Chain Monte Carlo for cointegrated time series ⋮ Differentiating the pseudo determinant ⋮ Multivariate isotropic random fields on spheres: nonparametric Bayesian modeling and \(L^p\) fast approximations ⋮ Bayesian inference over the Stiefel manifold via the Givens representation ⋮ Flexible Bayesian dynamic modeling of correlation and covariance matrices ⋮ Intrinsic Data Depth for Hermitian Positive Definite Matrices ⋮ Spectral analysis of high-dimensional time series ⋮ Intrinsic Wavelet Regression for Curves of Hermitian Positive Definite Matrices ⋮ Geometric Integration of Measure-Preserving Flows for Sampling

Uses Software

• Stan

Cites Work

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