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Advances in Markov chain Monte Carlo

Griffin Jim E and Stephens David A

in Bayesian Theory and Applications

Published in print:
2013
Published Online:
May 2013
ISBN:
9780199695607
eISBN:
9780191744167
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199695607.003.0007
Subject:
Mathematics, Probability / Statistics

This chapter traces some of the key developments that further developed the underpinning theory and potential applications of Markov chain Monte Carlo (MCMC) since the mid 1990s. In particular, it ... More


Markov chain Monte Carlo methods

Chib Siddhartha

in Bayesian Theory and Applications

Published in print:
2013
Published Online:
May 2013
ISBN:
9780199695607
eISBN:
9780191744167
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199695607.003.0006
Subject:
Mathematics, Probability / Statistics

This chapter provides a brief summary of Markov chain Monte Carlo (MCMC) methods. The chapter is organized as follows. Section 6.2 describes the Metropolis–Hastings algorithm and its generalized ... More


Combining Particle Filters with MH Samplers

Edward P. Herbst and Frank Schorfheide

in Bayesian Estimation of DSGE Models

Published in print:
2015
Published Online:
October 2017
ISBN:
9780691161082
eISBN:
9781400873739
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691161082.003.0009
Subject:
Economics and Finance, Econometrics

This chapter argues that in order to conduct Bayesian inference, the approximate likelihood function has to be embedded into a posterior sampler. It begins by combining the particle filtering methods ... More


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