Chapter

Bayesian Statistics and Linear Regression

Luc Bauwens, Michel Lubrano and Jean-François Richard

in Bayesian Inference in Dynamic Econometric Models

Published in print January 2000 | ISBN: 9780198773122
Published online September 2011 | e-ISBN: 9780191695315 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780198773122.003.0002

Series: Advanced Texts in Econometrics

Bayesian Statistics and Linear Regression

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This chapter presents the basic concepts and tools that are useful for modelling and for Bayesian inference. It defines density kernels useful for simplifying notation and computations and explains the likelihood principle and its implications for the Bayesian treatment of nuisance parameters. It discusses the notion of natural conjugate inference, which is an important tool of Bayesian analysis in the case of the exponential family, and provides details on the natural conjugate framework.

Keywords: Bayesian inference; density kernels; likelihood principle; nuisance parameters; conjugate inference; exponential family

Chapter.  9831 words.  Illustrated.

Subjects: Econometrics and Mathematical Economics

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