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Introduction to Bayesian econometrics

Autor: Edward Greenberg
Editorial: Cambridge ; New York : Cambridge University Press, 2008.
Edición/Formato:   Print book : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:
"This concise textbook is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It then turns to the definitions of the likelihood function, prior distributions, and posterior distributions. It explains how posterior distributions are
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Detalles

Tipo de material: Recurso en Internet
Tipo de documento: Libro/Texto, Recurso en Internet
Todos autores / colaboradores: Edward Greenberg
ISBN: 9780521858717 0521858712
Número OCLC: 144598187
Descripción: xv, 205 p. : ill. ; 27 cm.
Contenido: Basic concepts of probability and inference --
Posterior distributions and inference --
Prior distributions --
Classical simulation --
Basics of Markov chains --
Simulation by MCMC methods --
Linear regression and extensions --
Multivariate responses --
Time series --
Endogenous covariates and sample selection --
Appendix A : Probability distributions and Matrix theorems --
Appendix B : Computer programs for MCMC calculations.
Responsabilidad: Edward Greenberg.
Más información:

Resumen:

This book introduces the increasingly popular Bayesian approach to statistics to graduates and advanced undergraduates.  Leer más

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"This book provides an excellent introduction to Bayesian econometrics and statistics with many references to the recent literature that will be very helpful for students and others who have a good Leer más

 
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Datos enlazados


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schema:description"The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions, which leads to an explanation of classical and Markov chain Monte Carlo (MCMC) methods of simulation. The latter is proceeded by a brief introduction to Markov chains. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields."--BOOK JACKET."@en
schema:description"Basic concepts of probability and inference -- Posterior distributions and inference -- Prior distributions -- Classical simulation -- Basics of Markov chains -- Simulation by MCMC methods -- Linear regression and extensions -- Multivariate responses -- Time series -- Endogenous covariates and sample selection -- Appendix A : Probability distributions and Matrix theorems -- Appendix B : Computer programs for MCMC calculations."@en
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