<P style="MARGIN: 0px">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.</P> <P style="MARGIN: 0px"> </P> <P style="MARGIN: 0px">New technologies such as the Python PyMC library now make it possible to largely abstract Bayesian inference from deeper mathematics. <B>Bayesian Methods for Hackers </B>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:</P> <UL> <LI> <DIV style="MARGIN: 0px">The philosophy of Bayesian inference, the Bayesian "state of mind," and Bayesian inference in practice</DIV> </LI> <LI> <DIV style="MARGIN: 0px">How the Python PyMC library implements Bayesian techniques, freeing you to use them without first possessing a deep understanding of Bayesian mathematics</DIV> </LI> <LI> <DIV style="MARGIN: 0px">How to build on your growing application experience to gain a deeper theoretical understanding</DIV> </LI> </UL> <P style="MARGIN: 0px">To build your understanding, he guides you through many real-world applications, including:</P> <UL> <LI> <DIV style="MARGIN: 0px">Inferring behavior from text-message data</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Performing A/B testing with Bayesian methods</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Diagnosing and improving convergence</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Gaining insight from aggregated geographical data</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Developing statistical models to predict census form return rates</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Sequencing Reddit comments</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Improving machine learning</DIV> </LI> <LI> <DIV style="MARGIN: 0px">Predicting stock returns, and much more</DIV> </LI> </UL> <P style="MARGIN: 0px">Using <B>Bayesian Methods for Hackers</B>, 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.<BR> </P>