9781449370787-1449370780-Think Bayes: Bayesian Statistics in Python

Think Bayes: Bayesian Statistics in Python

ISBN-13: 9781449370787
ISBN-10: 1449370780
Edition: 1
Author: Allen B. Downey
Publication date: 2013
Publisher: O'Reilly Media
Format: Paperback 190 pages
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Book details

ISBN-13: 9781449370787
ISBN-10: 1449370780
Edition: 1
Author: Allen B. Downey
Publication date: 2013
Publisher: O'Reilly Media
Format: Paperback 190 pages

Summary

Think Bayes: Bayesian Statistics in Python (ISBN-13: 9781449370787 and ISBN-10: 1449370780), written by authors Allen B. Downey, was published by O'Reilly Media in 2013. With an overall rating of 4.2 stars, it's a notable title among other Data Processing (Databases & Big Data, Mathematical Analysis, Mathematics, Research) books. You can easily purchase or rent Think Bayes: Bayesian Statistics in Python (Paperback) from BooksRun, along with many other new and used Data Processing books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.62.

Description

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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