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Think Bayes

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Synopses & Reviews

Publisher Comments:

If you know how to program with Python and also know a little about probability, youre 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 youll 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 books 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.

Synopsis:

If you know how to program with Python, and know a little about probability, youre ready to tackle Bayesian statistics. This book shows you how to use Python code instead of math and Python objects to represent discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youll be able to apply these techniques to real-world problems.

  • Use your existing programming skills to learn and understand Bayesian statistics
  • Learn Bayess Theorem, computational statistics, estimation, odds, decision analysis, prediction, observer bias, and hypothesis testing
  • Use for loops in Python rather than complex integrals
  • Work with examples using M&Ms, Dungeons & Dragons dice, Reddit, hockey, paintball, and SAT Scores

About the Author

Allen Downey is a Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Masters and Bachelors degrees from MIT.

Product Details

ISBN:
9781449370787
Author:
Downey, Allen B.
Publisher:
O'Reilly Media
Author:
Downey, Allen
Subject:
Bayes;Bayes Theorem;Bayesian;Bayesian Computation,;Bayesian Statistics;Conditional Probability;Decision Analysis;Machine Learning;Math;Prediction;Probability;Python;Statistics;data;data science
Subject:
Mathematics - General
Edition Description:
Print PDF
Publication Date:
20131031
Binding:
TRADE PAPER
Language:
English
Pages:
210
Dimensions:
9.19 x 7 in

Related Subjects

Arts and Entertainment » Humor » General
Computers and Internet » Computer Languages » Python
Computers and Internet » Computers Reference » General
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics
Science and Mathematics » Mathematics » Software

Think Bayes New Trade Paper
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Product details 210 pages O'Reilly Media - English 9781449370787 Reviews:
"Synopsis" by ,

If you know how to program with Python, and know a little about probability, youre ready to tackle Bayesian statistics. This book shows you how to use Python code instead of math and Python objects to represent discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youll be able to apply these techniques to real-world problems.

  • Use your existing programming skills to learn and understand Bayesian statistics
  • Learn Bayess Theorem, computational statistics, estimation, odds, decision analysis, prediction, observer bias, and hypothesis testing
  • Use for loops in Python rather than complex integrals
  • Work with examples using M&Ms, Dungeons & Dragons dice, Reddit, hockey, paintball, and SAT Scores

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