The Resource Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Resource Information
The item Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
- Summary
- "Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--
- Language
- eng
- Label
- Bayesian data analysis
- Title
- Bayesian data analysis
- Statement of responsibility
- Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
- Language
- eng
- Summary
- "Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--
- Assigning source
- Provided by publisher
- Cataloging source
- DLC
- http://library.link/vocab/creatorName
- Gelman, Andrew
- Dewey number
- 519.5/42
- Illustrations
- illustrations
- Index
- index present
- LC call number
- QA279.5
- LC item number
- .G45 2014
- Literary form
- non fiction
- Nature of contents
- bibliography
- Series statement
- Chapman & Hall/CRC texts in statistical science
- http://library.link/vocab/subjectName
-
- Bayesian statistical decision theory
- MATHEMATICS / Probability & Statistics / General
- Label
- Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
- Bibliography note
- Includes bibliographical references and indexes
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent.
- Control code
- 859253474
- Dimensions
- 26 cm
- Edition
- Third edition.
- Extent
- 661 pages
- Isbn
- 9781439840955
- Lccn
- 2013039507
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- Other physical details
- illustrations
- Label
- Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
- Bibliography note
- Includes bibliographical references and indexes
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent.
- Control code
- 859253474
- Dimensions
- 26 cm
- Edition
- Third edition.
- Extent
- 661 pages
- Isbn
- 9781439840955
- Lccn
- 2013039507
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- Other physical details
- illustrations
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Bayesian-data-analysis-Andrew-Gelman-John-B./pIhgD3AKlAg/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Bayesian-data-analysis-Andrew-Gelman-John-B./pIhgD3AKlAg/">Bayesian data analysis, Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
