Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions
arXiv:1607.04751
Abstract
We introduce a fast and easy-to-implement simulation algorithm for a multivariate normal distribution truncated on the intersection of a set of hyperplanes, and further generalize it to efficiently simulate random variables from a multivariate normal distribution whose covariance (precision) matrix can be decomposed as a positive-definite matrix minus (plus) a low-rank symmetric matrix. Example results illustrate the correctness and efficiency of the proposed simulation algorithms.
To appear in Bayesian Analysis
References in corpus (7)
- Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
- A Complete Recipe for Stochastic Gradient MCMC
- Beta-Negative Binomial Process and Poisson Factor Analysis
- Fast simulation of truncated Gaussian distributions
- Bayesian separation of spectral sources under non-negativity and full additivity constraints
- Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
- Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC