Hierarchical Linear Models and Cell Data

Geoffrey Gordon
tech. report CMU-RI-TR-00-33, Robotics Institute, Carnegie Mellon University, March, 2000


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Abstract
Hierarchical linear models are a generalization of Bayesian linear regression. They differ from Bayesian regression in that they determine the width of the prior distribution for the regression coefficients automatically from the data. They are particularly appropriate when we want to answer questions about the typical weights in a regression instead of just the typical examples. This paper provides a tutorial on hierarchical linear models, then demonstrates their application to some biochemical data.

Notes

Text Reference
Geoffrey Gordon, "Hierarchical Linear Models and Cell Data," tech. report CMU-RI-TR-00-33, Robotics Institute, Carnegie Mellon University, March, 2000

BibTeX Reference
@techreport{Gordon_2000_3513,
   author = "Geoffrey Gordon",
   title = "Hierarchical Linear Models and Cell Data",
   booktitle = "",
   institution = "Robotics Institute",
   month = "March",
   year = "2000",
   number= "CMU-RI-TR-00-33",
   address= "Pittsburgh, PA",
}