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Abstract: this paper will be to study the performance of hybrid Monte Carlo in logistic regression problems with quasicomplete separation. The variation of hybrid Monte Carlo that we study is based on the "leapfrog" algorithm presented in Duane et al. (1987). As we will see, this method leads to a rapidly mixing Markov chain in this challenging problem, but in fact the method is also a very competitive and simple approach for fitting any Bayesian generalized linear model with a canonical link. The... (Update)
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...Neal (1994) The leapfrog algorithm has also been recently applied successfully in statistics. See Neal (1996) Gustafson (1997) and Ishwaran (1999) for examples. 1.1 HMC Versus Data Augmentation. A special feature of the Albert Chib (1993) data augmentation procedure is that...
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BibTeX entry: (Update)
H. Ishwaran (1999). Applications of hybrid Monte Carlo to Bayesian generalized linear models: quasicomplete separation and neural networks. Journal of Computational and Graphical Statistics, 8, 779--799. http://citeseer.ist.psu.edu/ishwaran99applications.html More
@article{ ishwaran99applications,
author = "Hemant Ishwaran",
title = "Applications of Hybrid {Monte Carlo} to {Bayesian} Generalized Linear Models: Quasicomplete Separation and Neural Networks",
journal = "Journal of Computational and Graphical Statistics",
volume = "8",
number = "4",
pages = "779--??",
year = "1999",
url = "citeseer.ist.psu.edu/ishwaran99applications.html" }
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