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Grapham: Graphical models with adaptive random walk Metropolis algorithms

Abstract:

Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms include the seminal Adaptive Metropolis algorithm adjusting the proposal covariance according to the history of the chain and a Metropolis algorithm adjusting the proposal scale based on the o...

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Publisher copy:
10.1016/j.csda.2009.09.001

Authors


Journal:
Computational Statistics and Data Analysis
Volume:
54
Issue:
1
Pages:
49-54
Publication date:
2010-01-01
DOI:
ISSN:
0167-9473
URN:
uuid:14d3a883-09cd-4f1d-9f5e-01b950010f00
Source identifiers:
487896
Local pid:
pubs:487896
Language:
English

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