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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Bayesian Methods for Hackers:   Probabilistic Programming and Bayesian Inference

Author(s):
  • Cameron Davidson-Pilon
  • Author: Cameron Davidson-Pilon
    • ISBN:9789353063641
    • 10 Digit ISBN:9353063647
    • Price:Rs. 565.00
    • Pages:256
    • Imprint:Pearson Education
    • Binding:Paperback
    • Status:Available


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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.

     

    Table of Content

    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 4: The Greatest Theorem Never Told

    Chapter 6: Getting Our Priorities Straight
    Chapter 7: Bayesian A/B Testing
     

    Salient Features

    Students will masterBayesian 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"