Ctrl+K

Overview

Part I: Bayesian methods for scientific modeling

Part II: Advanced Bayesian methods

10. Overview of Part II
11. Discrepancy Models
12. Assigning probabilities
13. 📥 Dealing with outliers
14. Bayes goes linear: History matching
15. Multi-model inference with Bayes
- 15.1. Model Selection
- 15.2. Model averaging and mixing

Part III: MCMC sampling

Part IV: Machine learning: A Bayesian perspective

21. Overview of Part IV
22. Overview of Gaussian processes
23. Logistic Regression
- 23.5. Machine Learning: First Examples
- 23.6. Exercise: Logistic Regression and neural networks
24. Machine learning: Overview
25. ANNs in the large-width limit (ANNFT)
- 25.3. 📥 Distributions of Randomly-Initialized ANNs
26. Bayesian neural nets
- 26.4. Demonstration: Variational Inference and Bayesian Neural Networks
- 26.5. Exercise: Bayesian neural networks
27. *Convolutional neural nets
- 27.6. Demonstration: Image recognition with Convolutional Neural Networks

Part V: Other topics

Backmatter

Appendix A: Reference

Appendix B: Scientific modeling

Appendix C: Getting started

41. Overview of Getting started material
42. 📥 Exercise: Jupyter notebooks and Python
43. More about Python and Jupyter notebooks
44. Setting up for using this Jupyter Book
- 44.1. Using Anaconda
- 44.2. Using GitHub

TALENT mini-projects

Multi-model inference with Bayes

15. Multi-model inference with Bayes#

By Christian Forssén, Dick Furnstahl, and Daniel Phillips