Bayesian Methods for Hackers by Cameron Davidson-Pilon
Bayesian Methods for Hackers - Table of Contents
- 1. Introduction to Bayesian Methods
- 2. A Little More on PyMC
- 3. Opening the Black Box of MCMC
- 4. The Greatest Theorem Never Told
- 5. Would You Rather Lose an Arm or a Leg?
- 6. Getting Our Priorities Straight, Probably
- 7. Probabilistic Programming & Bayesian Inference
What You Will Learn in Bayesian Methods for Hackers
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference Using Python and PyMC by Cameron Davidson-Pilon is a practical introduction to Bayesian statistics and probabilistic programming. The book is designed for learners who prefer coding-based intuition over heavy mathematical theory. It uses Python and the PyMC library to help readers understand how uncertainty can be modeled in real-world problems.
The book explains key concepts such as Bayesian inference, prior and posterior distributions, Markov Chain Monte Carlo (MCMC), and probabilistic modeling. It focuses on learning through hands-on examples like A/B testing, prediction problems, and decision-making under uncertainty. Instead of abstract theory, it emphasizes building intuition through experiments and code.
Overall, the book connects statistics, data science, and machine learning in a simple and applied way. It helps readers understand how probabilistic thinking works in modern AI systems and real-world analytics. It is especially useful for beginners who want to learn Bayesian methods, computational statistics, and practical probabilistic programming using Python.
Book Details & Specifications
Title:
Bayesian Methods for Hackers by Cameron Davidson-Pilon
Publisher:
Addison-Wesley
Year:
2015
Pages:
63
Type:
PDF
Language:
English
ISBN-10 #:
9353063647
ISBN-13 #:
978-9353063641
License:
MIT License
Amazon:
Amazon
About the Author: Cameron Davidson-Pilon
The author Cameron Davidson-Pilon
is a data scientist and author of Bayesian Methods for Hackers. He studied at the University of Waterloo and the Independent University of Moscow, with a background in applied mathematics, probability theory, and stochastic modeling. His work focuses on making complex statistical ideas easier to understand through practical coding examples.
His expertise includes Bayesian statistics, probabilistic programming, and data science applications. He has worked in industry, including Shopify, and contributed to open-source tools like lifelines for survival analysis in Python. He is known for simplifying machine learning and statistical inference into practical, code-based learning.
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