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Bayesian ideas and data analysis : an introduction for scientists and statisticians / Ronald Christensen...[et al.].

By: Contributor(s): Material type: TextTextSeries: Texts in statistical sciencePublication details: Boca Raton : CRC Press, ©2011.Description: xvii, 498 p. : ill. ; 27 cmISBN:
  • 9781439803547 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 000SA.161 23 C554
Contents:
1. Prologue -- 2. Fundamental ideas I -- 3. Integration versus simulation -- 4. Fundamental ideas II -- 5. Comparing populations -- 6. Simulations -- 7. Basic concepts of regression -- 8. Binominal regression -- 9. Linear regression -- 10. Correlated data -- 11. Count data -- 12. Time to event data -- 13. Time to event regression -- 14. Binary diagnostic tests -- 15. Nonparametric models -- Appendix A: Matrices and vectors -- Appendix B: Probability -- Appendix C: Getting started in R.
Summary: Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book's website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.161 C554 (Browse shelf(Opens below)) Available 137033
Total holds: 0

Includes bibliographical references and indexes.

1. Prologue --
2. Fundamental ideas I --
3. Integration versus simulation --
4. Fundamental ideas II --
5. Comparing populations --
6. Simulations --
7. Basic concepts of regression --
8. Binominal regression --
9. Linear regression --
10. Correlated data --
11. Count data --
12. Time to event data --
13. Time to event regression --
14. Binary diagnostic tests --
15. Nonparametric models --
Appendix A: Matrices and vectors --
Appendix B: Probability --
Appendix C: Getting started in R.

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book's website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data.

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