Think Bayes
Bayesian Statistics in Python
Publisher: O'Reilly Media
Released: September 2013
Pages: 210

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.

Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.

  • Use your existing programming skills to learn and understand Bayesian statistics
  • Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing
  • Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey
  • Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
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oreillyThink Bayes
 
5.0

(based on 3 reviews)

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Pros

  • Easy to understand (3)
  • Helpful examples (3)
  • Well-written (3)

Cons

    Best Uses

    • Intermediate (3)
    • Student (3)

    Reviewed by 3 customers

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    5.0

    Bayes without mathematical notation

    By ahmetRasit

    from Ankara, Turkey

    About Me Developer, Educator

    Verified Reviewer

    Pros

    • Concise
    • Easy to Follow
    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

      Best Uses

      • Intermediate
      • Novice
      • Student

      Comments about oreilly Think Bayes:

      I've always wanted to comprehend Bayesian Statistics whereas the mathematical notations scared me a lot. However, with this piece of art, finally I could finish a Bayes-focused book, without pain :) The author moves step by step and makes sure everything is clear for the reader. Examples are well-chosen real life examples, which make it much more easier to transform the acquired knowledge to the real world. Probably the best Bayes book for non-stats people. I definitely suggest this book for an introduction and/or a better comprehension of the subject.

       
      5.0

      Learn Statistics with Python

      By Jaype

      from Kenosha WI

      Verified Reviewer

      Pros

      • Accurate
      • Easy to understand
      • Helpful examples
      • Well-written

      Cons

        Best Uses

        • Expert
        • Intermediate
        • Student

        Comments about oreilly Think Bayes:

        I would recommend Think Bayes to anyone already familiar Python and statistics who wants to learn more about using both together. However this is not a book for beginners. You need to know something about probability theories and Python to get the most from it.

        The book is very well written and explains Bayes's Theorem in a way I could easily follow. The code examples I ran were very helpful.

        (9 of 9 customers found this review helpful)

         
        5.0

        An excellent introduction

        By John

        from Liverpool, UK

        About Me Data Analyst, Developer, Researcher

        Verified Reviewer

        Pros

        • Accurate
        • Concise
        • Easy to understand
        • For Multiple Audiences
        • Helpful examples
        • Well-written

        Cons

        • May Require Accompaniment
        • Not Totally Comprehensive

        Best Uses

        • Intermediate
        • Novice
        • Some Expert Developers
        • Some Expert Statisticians
        • Student

        Comments about oreilly Think Bayes:

        An excellent introduction to Bayesian Statistics, which I gained a lot from reading and coding alongside. I bought this title to learn more about using Python for stats.

        This book provides a solid definition of the concepts underlying the core of Bayesian statistics. It is not designed as a purely theoretical description of Bayesian concepts, and instead may profitably be read as a follow-along introduction to how to think and operate with Bayesian methods.

        This makes the title appropriate to individuals of any ability whose programming skills are strong but whose statistical analysis experience or understanding of Bayesian statistics is weak. Some non-statisticians may benefit from reading this title alongside a more theoretical book on Bayes. The title may also suit statisticians of varying ability looking for guidance in how to apply techniques. It should be possible to do so without needing another title on Python.

        The examples are interesting and well-selected (this becomes increasingly true as the book progresses) and illustrate the informative power of Bayesian statistical techniques in a wide range of contexts. The author provides additional resources for the interested reader.

        The title does progress to discuss some advanced concepts, including Approximate Bayesian Computation (ABC), however it should not be expected to give a complete overview of Bayesian methods. For a more complete
        or theoretical review of methods (eg. simulation options), the advanced reader is advised to use another title in conjunction with Think Bayes.

        This title is easy to follow and surprisingly pleasant to read; the occasional sprinkling of dry wit being quite pleasant. I admit to having passed across my tablet every so often to share an example or amusing passage.

        In short, this title offers value to a number of audiences. It is a well-written, computationally-couched introduction to Bayes. I will certainly be purchasing more titles by Allen Downey, and have recommended Think Bayes to a number of colleagues and associates.

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