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Type: Package
Title: Sampling Multivariate Normal Distribution under Linear Constraints
Version: 0.1
Date: 2021-10-25
Maintainer: Yunyi Shen <yshen99@wisc.edu>
Description: Sample truncated multivariate Normal distribution following Gessner, A., Kanjilal, O., & Hennig, P. (2019). Integrals over Gaussians under Linear Domain Constraints. 108. <doi:10.48550/arXiv.1910.09328>.
License: GPL-3
Imports: Rcpp (≥ 1.0.7), MASS
LinkingTo: Rcpp, RcppArmadillo
URL: https://github.com/YunyiShen/linconGaussR
BugReports: https://github.com/YunyiShen/linconGaussR/issues
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-10-25 16:28:46 UTC; yunyi
Author: Yunyi Shen ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2021-10-26 14:10:02 UTC

Sample Gaussian distribution with linear constraints Taking truncated sample of Gaussian distribution over a linear constraint domain.

Description

Sample Gaussian distribution with linear constraints Taking truncated sample of Gaussian distribution over a linear constraint domain.

Usage

linconGauss(
  n,
  A,
  b,
  Sigma,
  mu,
  x_init = NULL,
  intersection = TRUE,
  n_retry_init = 1000,
  nskp = 5
)

Arguments

n

number of samples to take

A

a matrix with M by D dimensions, the linear constraints, such that Ax+b>=0

b

the offset of the linear constraints with dimension M such that Ax+b>=0

Sigma

covariance matrix of the Gaussian

mu

mean vector of the Gaussian

x_init

the sample to start with, if NULL, a sample will be drawn using rejection method

intersection

bool whether sample from the intersection or the union of the linear constraints, default true, sample from the intersection

n_retry_init

how many times to try finding a initial value

nskp

how many sample to skip during the sampling routine

Value

a matrix with truncated sample, row as samples

Examples

my_sample <- linconGauss(100, diag(2),c(0,0),diag(2),c(0,0))
MASS_sample <- MASS::mvrnorm(1000,c(0,0),diag(2))
plot(MASS_sample)
points(my_sample,col = "red")
abline(h=0)
abline(v=0)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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