From MFKP_wiki

Jump to: navigation, search

Hierarchical Bayesian modeling

Alan M. Zaslavsky

edited by: S. James Press

Excerpt (Disclaimer)


The following text is a small excerpt from the original publication. Within the general INRMM-MiD goal of indexing useful meta-information on INRMM related publications, this excerpt is intended as a handy summary of some potentially interesting aspects of the publication. However, the excerpt is surely incomplete and some key aspects may be missing or their correct interpretation may require the full publication to be carefully read. Please, refer to the full publication for any detail.


Introduction. Hierarchical modeling is a widely used approach to building complex models by specifying a series of more simple conditional distributions. It naturally lends itself to Bayesian inference, especially using modern tools for Bayesian computation. In this chapter we first present essential concepts of hierarchical modeling, and then suggest its generality by presenting a series of widely used specific models. [...]

Summary. In this chapter we have introduced hierarchical modeling as a very general approach to specifying complex models through a sequence of more simple stages. Hierarchical models are useful for modeling coIlections of observations with a simple or complex exchangeability structure. Using fully Bayesian approaches, both general parameters (characterizing the entire population) and parameters specific to individual units (as in smaltarea estimation or profiling) can be estimated. Modem Bayesian computational approaches are well adapted to estimation of such models. [...]

In Subjective and Objective Bayesian Statistics: Principles, Models, and Applications, Second Edition (25 November 2002), pp. 336-358, 
Key: INRMM:14546817



Article-Level Metrics (Altmetrics)
Digital Object Identifier

Available versions (may include free-access full text)

DOI, Pubget, PubMed (Search)

Versions of the publication are also available in Google Scholar.
Google Scholar code: GScluster:12676065703777617964

Works citing this publication (including grey literature)

An updated list of who cited this publication is available in Google Scholar.
Google Scholar code: GScites:12676065703777617964

Further search for available versions

Search in ResearchGate (or try with a fuzzier search in ResearchGate)
Search in Mendeley (or try with a fuzzier search in Mendeley)

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core
Metadata search: CrossRef DOI, DataCite DOI

Digital preservation of this INRMM-MiD record

Internet Archive

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.