The next generation of really difficult problems will be statistical, not deterministic: the solutions will be buried beneath layers of noise. Bayesian methods offer data scientists powerful flexibility in solving these brutally complex problems. However, Bayesian methods have traditionally required deep mastery of complicated math and advanced algorithms, placing them off-limits to many who could benefit from them.
New technologies such as the Python PyMC library now make it possible to largely abstract Bayesian inference from deeper mathematics. Bayesian Methods for Hackers is the first book built upon this approach. Using realistic and relevant examples, it shows programmers how to solve many common problems with Bayesian methods, even if they have only modest mathematical backgrounds. Cameron Davidson-Pilon demystifies all facets of Bayesian programming, including:
- The philosophy of Bayesian inference, the Bayesian "state of mind," and Bayesian inference in practice
- How the Python PyMC library implements Bayesian techniques, freeing you to use them without first possessing a deep understanding of Bayesian mathematics
- How to build on your growing application experience to gain a deeper theoretical understanding
To build your understanding, he guides you through many real-world applications, including:
- Inferring behavior from text-message data
- Performing A/B testing with Bayesian methods
- Diagnosing and improving convergence
- Gaining insight from aggregated geographical data
- Developing statistical models to predict census form return rates
- Sequencing Reddit comments
- Improving machine learning
- Predicting stock returns, and much more
Using Bayesian Methods for Hackers, you can start leveraging powerful Bayesian tools right now -- gradually deepening your theoretical knowledge while you're already achieving powerful results in areas ranging from marketing to finance.