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[ascl:1602.005] LRGS: Linear Regression by Gibbs Sampling

LRGS (Linear Regression by Gibbs Sampling) implements a Gibbs sampler to solve the problem of multivariate linear regression with uncertainties in all measured quantities and intrinsic scatter. LRGS extends an algorithm by Kelly (2007) that used Gibbs sampling for performing linear regression in fairly general cases in two ways: generalizing the procedure for multiple response variables, and modeling the prior distribution of covariates using a Dirichlet process.

Code site:
https://github.com/abmantz/lrgs https://cran.r-project.org/web/packages/lrgs/index.html
Described in:
http://adsabs.harvard.edu/abs/2016MNRAS.457.1279M
Bibcode:
2016ascl.soft02005M

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ascl:1602.005
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