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Bayesian Inference in Dynamic Econometric Models$
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Luc Bauwens, Michel Lubrano, and Jean-François Richard

Print publication date: 2000

Print ISBN-13: 9780198773122

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780198773122.001.0001

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Bayesian Statistics and Linear Regression

Bayesian Statistics and Linear Regression

Chapter:
(p.35) 2 Bayesian Statistics and Linear Regression
Source:
Bayesian Inference in Dynamic Econometric Models
Author(s):

Luc Bauwens

Michel Lubrano

Jean-François Richard

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198773122.003.0002

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

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