具体描述
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.
作者简介
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 Master’s and Bachelor’s degrees from MIT.
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用户评价
给代码配上流程图解，思路可以更清晰。
评分深入浅出，通俗易懂，配合着github上的代码，算是对bayes有了初步的了解。
评分最后一章实在看不下去了，对具体的模型没有了解，思维方式倒是熟悉了
评分废话太多...
评分太简单了…… 没学到啥
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