Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, 1st edition

Published by Addison-Wesley Professional (September 30, 2015) © 2016

  • Cameron Davidson-Pilon
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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.
  • 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: Would You Rather Lose an Arm or a Leg?
  • Chapter 6: Getting Our Priorities Straight
  • Chapter 7: Bayesian A/B Testing

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