The next generation of problems will not have deterministic solutions the solutions will be statistical that rely on mountains or mounds, of data. Bayesian methods offer a very flexible and extendible framework to solve these types of problems. For programming students with minimal background in mathematics, this example-heavy guide emphasizes the New technologies that have allowed the inference to be abstracted from complicated underlying mathematics. Using Bayesian Methods for Hackers, students can start leveraging powerful Bayesian tools right now gradually deepening their theoretical knowledge while already achieving powerful results in areas ranging from marketing to finance. Students will master Bayesian techniques that will play an increasingly crucial role in every data scientist's toolkit
Shows students how to solve statistically-based problems relying on mountains of data
Teaches through realistic (non-toy) examples built with the Python PyMC library, including start-to-finish application case studies
Gives an intuitive understanding of key concepts such as clustering, convergence, autocorrelation and thinning Chapter 1: the Philosophy of Bayesian Inference
Chapter 2: A Little More on PyMC
Chapter 3: Opening the Black Box of MCMC
Chapter 4: the Greatest Theorem Never Told
Chapter 5: the Greatest Theorem Never Told
Chapter 6: Getting Our Priorities Straight
Chapter 7: Bayesian A/B Testing.