Ctrl+K

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: A Bayesian perspective

23. Overview of Part IV
24. Overview of Gaussian processes
25. Logistic Regression
- 25.5. Machine Learning: First Examples
- 25.6. Exercise: Logistic Regression and Neural Networks
26. Bayesian Optimization
27. Machine learning: Overview
28. ANNs in the large-width limit (ANNFT)
- 28.11. Demo: Distributions of Randomly-Initialized ANNs
29. Bayesian neural nets
- 29.4. Demo: Variational Inference and Bayesian Neural Networks
- 29.5. Exercise: Bayesian neural networks
30. *Convolutional neural nets

Part V: Other topics

Backmatter

Appendix A: Statistics

Appendix B: Scientific modeling

Appendix C: Getting started

42. Overview of Getting started material
43. Setting up for interactive use of this Jupyter book
- 43.1. Using git for cloning the book repository
- 43.2. Setting up your Python enviroment
44. Jupyter notebooks and Python
45. Guides on Jupyter notebooks and Python

TALENT mini-projects

pocoMC

22.3. pocoMC#

Demo to be added. See pocoMC.

By Christian Forssén, Dick Furnstahl, and Daniel Phillips