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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference by Cameron Davidson-Pilon (Paperback, 2015)

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However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model.

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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.