Introduction to hierarchical Bayesian modeling for ecological data (Record no. 200072)

000 -LEADER
fixed length control field 02144cam a2200193 a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120525s2013 flua b 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781584889199
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 577.0727
Item number P2I6
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Parent, E.
9 (RLIN) 317855
245 10 - TITLE STATEMENT
Title Introduction to hierarchical Bayesian modeling for ecological data
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Boca Raton
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2013
300 ## - PHYSICAL DESCRIPTION
Extent xxi, 405 p.
490 0# - SERIES STATEMENT
Series statement Chapman & Hall/CRC Applied Environmental Statistics
520 ## - SUMMARY, ETC.
Summary, etc Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models.

The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website.

This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.(https://www.crcpress.com/product/isbn/9781584889199)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Ecology - Statistical methods
9 (RLIN) 317852
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Bayesian statistical decision theory
9 (RLIN) 317853
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Rivot, Etienne
9 (RLIN) 317854
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Full call number Barcode Date last seen Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library   2015-04-30 Astha Book Agency 4746.41 Slot 1706 (2 Floor, East Wing) 577.0727 P2I6 189313 2015-04-30 5933.01 Books

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