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Bayesian latent variable regression via Gibbs sampling: methodology and practical aspects
Author(s) -
Chen Hongshu,
Bakshi Bhavik R.,
Goel Prem K.
Publication year - 2007
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1083
Subject(s) - gibbs sampling , latent variable , bayesian probability , computer science , sampling (signal processing) , linear regression , bayesian linear regression , statistics , variable (mathematics) , econometrics , data mining , mathematics , artificial intelligence , machine learning , bayesian inference , filter (signal processing) , mathematical analysis , computer vision
Bayesian latent variable regression (BLVR) aims to utilize all available information for empirical modeling via a Bayesian framework. Such information includes prior knowledge about the underlying variables, model parameters and measurement error distributions. This paper improves upon the existing optimization‐based BLVR (BLVR‐OPT) method [1] by developing a sampling‐based Bayesian latent variable regression (BLVR‐S) method that relies on Gibbs sampling. Use of the sampling‐based framework not only provides point estimates, but its ability to generate samples that represent the posterior distribution of the unknown variables, also readily provides error bounds. Features and advantages of this method are demonstrated via examples based on simulated data and real Near‐Infrared (NIR) spectroscopy data. Practical aspects of Bayesian modeling such as determining when the extra computation may be worth the effort are addressed by an empirical study of the effects of the amount of training data and signal to noise ratio (SNR). The benefits of BLVR seem to be most significant when the number of measurements is limited and when noise in output variables is relatively large. Copyright © 2007 John Wiley & Sons, Ltd.