Exact Bayesian Inference in Regression

EBIR Exact Bayesian Inference in Regression

Variable Selection


EBIR is an exact Bayesian algorithm applicable to both variable selection and model averaging problems.
This algorithm improves the time complexity of exact inference
with a recursive algorithm that uses components of one sub-model to rapidly generate
another with a time complexity of O(m^2), where m is the number candidate variables



Reference: On efficient calculations for Bayesian variable selection, (2012) Eric Ruggieri, and Charles E. Lawrence,
Computational Statistics and Data Analysis, 56: 1319-1332, doi:10.1016/j.csda.2011.09.026


NSF Award Number: 1025438 NSF
Award Title: CMG Collaborative Research: Probabilistic Stratigraphic Alignment and Dating of Paleoclimate Data
PI/Co-PI Name: Charles Lawrence


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