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Probability in Practice: A Hands-On Journey with Python

Part 1 - Foundations of Probability

Chapter 1: Introduction to Probability and Python Setup
Chapter 2: The Language of Probability: Sets, Sample Spaces, and Events
Chapter 3: Counting Techniques: Permutations and Combinations

Part 2 - Conditional Probability and Independence

Chapter 4: Conditional Probability
Chapter 5: Bayes’ Theorem and Independence

Part 3 - Random Variables and Distributions

Chapter 6: Discrete Random Variables
Chapter 7: Common Discrete Distributions
Chapter 8: Continuous Random Variables
Chapter 9: Common Continuous Distributions

Part 4 - Multiple Random Variables

Chapter 10: Joint Distributions
Chapter 11: Independence, Covariance, and Correlation
Chapter 12: Functions of Multiple Random Variables

Part 5 - Limit Theorems and Their Significance

Chapter 13: The Law of Large Numbers (LLN)
Chapter 14: The Central Limit Theorem (CLT)

Part 6 - Advanced Topics and Applications

Chapter 15: Introduction to Bayesian Inference
Chapter 16: Introduction to Markov Chains
Chapter 17: Monte Carlo Methods
Chapter 18: (Optional) Further Explorations

Part 7 - Exercises

Exercises for Chapter 4 (Part A)
Exercises for Chapter 4 (Part B).

Appendices

Appendix A: Python/Jupyter Setup Deep Dive
Appendix B: Essential Library Reference
Appendix C: Mathematical Notation Summary
Appendix D: Further Reading and Resources
Appendix E: Summary of Formulas

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Index

By Chris Snow

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