65
IRUS Total
Downloads
  Altmetric

Hamiltonian Monte Carlo for hierarchical models

File Description SizeFormat 
1312.0906v1.pdfAccepted version1.72 MBAdobe PDFView/Open
Title: Hamiltonian Monte Carlo for hierarchical models
Authors: Betancourt, MJ
Girolami, M
Item Type: Chapter
Abstract: Hierarchical modeling provides a framework for modeling the complex interactions typical of problems in applied statistics. By capturing these relationships, however, hierarchical models also introduce distinctive pathologies that quickly limit the efficiency of most common methods of in- ference. In this paper we explore the use of Hamiltonian Monte Carlo for hierarchical models and demonstrate how the algorithm can overcome those pathologies in practical applications.
Editors: Upandhyay, SK
Singh, U
Dey, DK
Loganathan, A
Issue Date: 31-Dec-2015
URI: http://hdl.handle.net/10044/1/72630
ISBN: 9781482235128
Publisher: Taylor & Francis Group
Start Page: 79
End Page: 100
Journal / Book Title: Current Trends in Bayesian Methodology with Applications
Copyright Statement: © 2015 by Taylor & Francis Group, LLC. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in Current Trends in Bayesian Methodology with Applications in 2015.
Keywords: stat.ME
stat.ME
stat.ME
stat.ME
Notes: 11 pages, 12 figures
Publication Status: Published
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences