Название: Think Bayes: Bayesian Statistics in Python, 2nd Edition
Автор: Allen B. Downey
Издательство: O’Reilly Media
Год: 2021
Страниц: 338
Язык: английский
Формат: pdf (true), epub
Размер: 18.5 MB, 10.1 MB

If you know how to program, you'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 rather than 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 there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start.

Use your programming skills to learn and understand Bayesian statistics
Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing
Get started with simple examples, using coins, dice, and a bowl of cookies
Learn computational methods for solving real-world problems

Who Is This Book For?
To start this book, you should be comfortable with Python. If you are familiar with NumPy and pandas, that will help, but I’ll explain what you need as we go. You don’t need to know calculus or linear algebra. You don’t need any prior knowledge of statistics. In Chapter 1, I define probability and introduce conditional probability, which is the foundation of Bayes’s theorem. Chapter 3 introduces the probability distribution, which is the foundation of Bayesian statistics. In later chapters, we use a variety of discrete and continuous distributions, including the binomial, exponential, Poisson, beta, gamma, and normal distributions. I will explain each distribution when it is introduced, and we will use SciPy to compute them, so you don’t need to know about their mathematical properties.