Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Astrostatistics
- 2 Prerequisites
- 3 Frequentist vs. Bayesian Methods
- 4 Normal Linear Models
- 5 GLMs Part I – Continuous and Binomial Models
- 6 GLMs Part II – Count Models
- 7 GLMs Part III – Zero-Inflated and Hurdle Models
- 8 Hierarchical GLMMs
- 9 Model Selection
- 10 Astronomical Applications
- 11 The Future of Astrostatistics
- Appendix A Bayesian Modeling using INLA
- Appendix B Count Models with Offsets
- Appendix C Predicted Values, Residuals, and Diagnostics
- References
- Index
- Plate section
Appendix C - Predicted Values, Residuals, and Diagnostics
Published online by Cambridge University Press: 11 May 2017
Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Astrostatistics
- 2 Prerequisites
- 3 Frequentist vs. Bayesian Methods
- 4 Normal Linear Models
- 5 GLMs Part I – Continuous and Binomial Models
- 6 GLMs Part II – Count Models
- 7 GLMs Part III – Zero-Inflated and Hurdle Models
- 8 Hierarchical GLMMs
- 9 Model Selection
- 10 Astronomical Applications
- 11 The Future of Astrostatistics
- Appendix A Bayesian Modeling using INLA
- Appendix B Count Models with Offsets
- Appendix C Predicted Values, Residuals, and Diagnostics
- References
- Index
- Plate section
- Type
- Chapter
- Information
- Bayesian Models for Astrophysical DataUsing R, JAGS, Python, and Stan, pp. 377 - 379Publisher: Cambridge University PressPrint publication year: 2017