Using a Bayesian hierarchical linear mixing model to estimate botanical mixtures
type: Item
instance of: scholarly article
title: Using a Bayesian hierarchical linear mixing model to estimate botanical mixtures
zbMATH Open document ID: 1391.62280
author: Napoleón Vargas Jurado
author: Kent M. Eskridge
author: Stephen D. Kachman
author: Ronald M. Lewis
publication date: 2018-08-01T00:00:00Z
Mathematics Subject Classification ID: 62P12
zbMATH DE Number: 6912126
zbMATH Keywords: diet composition
zbMATH Keywords: forage mixtures
zbMATH Keywords: plant-wax markers
zbMATH Keywords: simplex
zbMATH Keywords: Gibbs sampler
Wikidata QID: Q59944532
describes a project that uses: MixSIAR
describes a project that uses: R
describes a project that uses: RcppArmadillo
describes a project that uses: nnls
describes a project that uses: MixSIR
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: s13253-018-0318-9
OpenAlex ID: W2792447816
cites work: Q4207468
cites work: Estimation of covariance matrices based on hierarchical inverse-Wishart priors
cites work: Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery
cites work: RcppArmadillo: accelerating R with high-performance C++ linear algebra
cites work: Isometric logratio transformations for compositional data analysis
cites work: A Bayesian approach to estimating linear mixtures with unknown covariance structure
cites work: Q4879581
cites work: Regression for Compositional Data by using Distributions Defined on the Hypersphere
cites work: Managing the essential zeros in quantitative fatty acid signature analysis
cites work: BEMMA: a hierarchical Bayesian end-member modeling analysis of sediment grain-size distributions
DOI: 10.1007/S13253-018-0318-9
published in: Journal of Agricultural, Biological and Environmental Statistics
description: scientific article; zbMATH DE number 6912126