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Overview

Part I: Bayesian methods for scientific modeling

Part II: Advanced Bayesian methods

10. Overview of Part II
11. Assigning probabilities
12. Dealing with outliers
13. Bayes goes linear — History matching
- 13.1. Bayes linear methods
- 13.2. Iterative history matching
14. Model selection
15. Discrepancy Models with GPs
16. Model averaging and mixing

Part III: MCMC sampling

Part IV: Machine learning basics

23. Overview of Part IV
24. Machine learning overview
25. Logistic Regression
- 25.5. Machine Learning: First Examples
- 25.6. Exercise: Logistic Regression and Neural Networks
26. Artificial neural networks (ANNs)
27. *Convolutional neural nets
28. Problems for Part IV

Part V: Probabilistic machine learning

29. Overview of Part V
30. Bayesian neural nets
- 30.4. Demo: Variational Inference and Bayesian Neural Networks
31. Gaussian processes
32. ANNs in the large-width limit (ANNFT)
33. Bayesian Optimization
34. Dimensionality reduction and emulators
35. Problems for Part V
- 35.1. Bayesian neural networks

Backmatter

Appendix A: Statistics

Appendix B: Scientific modeling

Appendix C: Getting started

45. Overview of Getting started material
46. Setting up for interactive use of this Jupyter book
- 46.1. Using git for cloning the book repository
- 46.2. Setting up your Python enviroment
47. Jupyter notebooks and Python
48. Guides on Jupyter notebooks and Python

TALENT mini-projects

Appendix: field theory formalism

32.9. Appendix: field theory formalism#

By Christian Forssén, Dick Furnstahl, and Daniel Phillips