Bayesian models for astrophysical data : using R, JAGS, Python, and Stan / Joseph M. Hilbe, Rafael S. de Souza and Emille E.O. Ishida.
Material type:
TextPublication details: Cambridge : Cambridge University Press, 2017.Description: xvii, 393 pages, 18 unnumbered pages of plates : illustrations (some color) ; 26 cmISBN: - 9781107133082 (hbk : alk. paper)
- 000SB:520.1 23 H641
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SB:520.1 H641 (Browse shelf(Opens below)) | Available | 138040 |
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| 000SB:519.5 In59 Actuarial statistics | 000SB:519.5 In59 Actuarial statistics | 000SB:519.5 In59 Actuarial statistics | 000SB:520.1 H641 Bayesian models for astrophysical data : using R, JAGS, Python, and Stan / | 000SB:520 B293 Models for functional relationship illustrated on astronomical data | 000SB:520 B293 Models for functional relationship illustrated on astronomical data | 000SB:520 C495 Statistical methods for astronomical data analysis / |
Includes bibliographical references and index.
Preface;
1. Astrostatistics;
2. Prerequisites;
3. Frequentist vs Bayesian methods;
4. Normal linear models;
5. GLM part I - continuous and binomial models;
6. GLM part II - count models;
7. GLM part III - zero-inflated and hurdle models;
8. Hierarchical GLMMs;
9. Model selection;
10. Astronomical applications;
11. The future of astrostatistics;
Appendices;
Index.
The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretation to address scientific questions. A must-have for astronomers, the book's concrete approach will also be attractive to researchers in the sciences more broadly.
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