12. Solutions

Kapitel in diesem Buch

1. Frontmatter i
2. Contents v
3. Preface ix
4. I. Fundamentals
5. 1. Preview 1
6. 2. Deterministic Models 17
7. 3. Principles of Probability 29
8. 4. Likelihood 71
9. 5. Simple Bayesian Models 79
10. 6. Hierarchical Bayesian Models 107
11. II. Implementation
12. Introduction 143
13. 7. Markov Chain Monte Carlo 145
14. 8. Inference from a Single Model 181
15. 9. Inference from Multiple Models 209
16. III. Practice in Model Building
17. Introduction 231
18. 10. Writing Bayesian Models 233
19. 11. Problems 243
20. 12. Solutions 251
21. Afterword 273
22. Acknowledgments 277
23. Appendix A. 279
24. Bibliography 283
25. Index 293