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Think Bayes

Bayesian Statistics in Python - Second Edition

Ocena książki

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  • 335 stron
  • 12 godzin czytania

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If you know how to program with Python, youâ??re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youâ??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 bookâ??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, dice, and a bowl of cookies Learn computational methods for solving real-world problems

Wydanie

Zakup książki

Think Bayes, Allen B. Downey

Język
Rok wydania
2021
Oprawa
(miękka)
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4,2
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Tytuł
Think Bayes
Podtytuł
Bayesian Statistics in Python - Second Edition
Język
angielski
Rok wydania
2021
Oprawa
miękka
Liczba stron
335
ISBN10
149208946X
ISBN13
9781492089469
Seria
Ocena
4,15 z 5
Opis
If you know how to program with Python, youâ??re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and youâ??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 bookâ??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, dice, and a bowl of cookies Learn computational methods for solving real-world problems