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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
17. Problems for Part II

Part III: MCMC sampling

Part IV: Machine learning basics

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

Part V: Probabilistic machine learning

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

Backmatter

Appendix A: Statistics

Appendix B: Scientific modeling

Appendix C: Getting started

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

TALENT mini-projects

pocoMC

23.3. pocoMC#

Demo to be added. See pocoMC.

By Christian Forssén, Dick Furnstahl, and Daniel Phillips