Description
Book Synopsis: 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.
Details
Looking to expand your statistical analysis skills? With the "Think Bayes: Bayesian Statistics in Python" book, you can take your Python programming knowledge to the next level and delve into the world of Bayesian statistics. No need to get overwhelmed with complex mathematical notations - this book teaches you how to use Python code to solve statistical problems.
With the increasing importance of Bayesian statistical methods, it's essential to stay ahead of the game. Unfortunately, there are limited resources available for beginners. That's where this book comes in, based on Allen Downey's undergraduate classes. You'll gain a solid foundation in Bayesian statistics and apply these techniques to real-world problems.
Whether it's estimation, prediction, decision analysis, evidence, or hypothesis testing, this book covers it all. It starts with simple examples like coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey, allowing you to grasp the concepts easily. From there, you'll move on to more complex problems like interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
Don't miss out on this opportunity to level up your statistical analysis skills. Get started with "Think Bayes: Bayesian Statistics in Python" today and uncover new insights from your data.
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