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:
To build your understanding, he guides you through many real-world applications, including:
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.