Book Description
Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron DavidsonPilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
DavidsonPilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
Coverage includes
• Learning the Bayesian “state of mind” and its practical implications
• Understanding how computers perform Bayesian inference
• Using the PyMC Python library to program Bayesian analyses
• Building and debugging models with PyMC
• Testing your model’s “goodness of fit”
• Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works
• Leveraging the power of the “Law of Large Numbers”
• Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning
• Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes
• Selecting appropriate priors and understanding how their influence changes with dataset size
• Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough
• Using Bayesian inference to improve A/B testing
• Solving data science problems when only small amounts of data are available
Cameron DavidsonPilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
Table of Contents
 About This eBook
 Title Page
 Copyright Page
 Dedication Page
 Contents
 Foreword
 Preface
 Acknowledgments
 About the Author
 1. The Philosophy of Bayesian Inference

2. A Little More on PyMC
 2.1 Introduction

2.2 Modeling Approaches
 2.2.1 Same Story, Different Ending
 2.2.2 Example: Bayesian A/B Testing
 2.2.3 A Simple Case
 2.2.4 A and B Together
 2.2.5 Example: An Algorithm for Human Deceit
 2.2.6 The Binomial Distribution
 2.2.7 Example: Cheating Among Students
 2.2.8 Alternative PyMC Model
 2.2.9 More PyMC Tricks
 2.2.10 Example: Challenger Space Shuttle Disaster
 2.2.11 The Normal Distribution
 2.2.12 What Happened the Day of the Challenger Disaster?
 2.3 Is Our Model Appropriate?
 2.4 Conclusion
 2.5 Appendix
 2.6 Exercises
 2.7 References
 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
 6.1 Introduction
 6.2 Subjective versus Objective Priors
 6.3 Useful Priors to Know About
 6.4 Example: Bayesian MultiArmed Bandits
 6.5 Eliciting Prior Distributions from Domain Experts
 6.6 Conjugate Priors
 6.7 Jeffreys Priors
 6.8 Effect of the Prior as N Increases
 6.9 Conclusion
 6.10 Appendix
 6.11 References
 7. Bayesian A/B Testing
 Glossary
 Index
 Code Snippets
Product Information
 Title: Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
 Author(s):
 Release date: October 2015
 Publisher(s): AddisonWesley Professional
 ISBN: 9780133902914