The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.
Most books on Bayesian statistics use math notation and present ideas using mathematical concepts like calculus. This book uses Python code and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are loops or array operations.
I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to mathematical analysis.
Also, it provides a smooth path from simple examples to 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.
Most chapters in this book are motivated by a real-world problem, so they involve some degree of modeling. Before we can apply Bayesian methods (or any other analysis), we have to make decisions about which parts of the real-world system to include in the model and which details we can abstract away.
For example, in Chapter 8, the motivating problem is to predict the winner of a soccer (football) game. I model goal-scoring as a Poisson process, which implies that a goal is equally likely at any point in the game. That is not exactly true, but it is probably a good enough model for most purposes.
I think it is important to include modeling as an explicit part of problem solving because it reminds us to think about modeling errors (that is, errors due to simplifications and assumptions of the model).
Many of the methods in this book are based on discrete distributions, which makes some people worry about numerical errors. But for real-world problems, numerical errors are almost always smaller than modeling errors.
Furthermore, the discrete approach often allows better modeling decisions, and I would rather have an approximate solution to a good model than an exact solution to a bad model.
Working with the code#
Reading this book will only get you so far; to really understand it,
you have to work with the code.
The original form of this book is a series of Jupyter notebooks.
After you read each chapter, I encourage you to run the notebook and work
on the exercises.
If you need help, my solutions are available.
There are several ways to run the notebooks:
If you have Python and Jupyter installed, you can download the notebooks and run them on your computer.
If you don’t have a programming environment where you can run Jupyter notebooks, you can use Colab, which lets you run Jupyter notebooks in a browser without installing anything.
To run the notebooks on Colab, start from this landing page, which has links to all of the notebooks.
If you already have Python and Jupyter, you can download the notebooks as a Zip file.
If you don’t have Python and Jupyter already, I recommend you install Anaconda, which is a free Python distribution that includes all the packages you’ll need. I found Anaconda easy to install. By default it installs files in your home directory, so you don’t need administrator privileges. You can download Anaconda from this site.
Anaconda includes most of the packages you need to run the code in this book. But there are a few additional packages you need to install.
To make sure you have everything you need
(and the right versions), the best option is to create a Conda
Download this Conda environment file and run the following commands to create and activate an environment called
conda env create -f environment.yml conda activate ThinkBayes2
If you don’t want to create an environment just for this book, you can install what you need using Conda. The following commands should get everything you need:
conda install python jupyter pandas scipy matplotlib pip install empiricaldist
If you don’t want to use Anaconda, you will need the following packages:
Jupyter to run the notebooks, https://jupyter.org/;
NumPy for basic numerical computation, https://www.numpy.org/;
SciPy for scientific computation, https://www.scipy.org/;
Pandas for working with data, https://pandas.pydata.org/;
matplotlib for visualization, https://matplotlib.org/;
empiricaldist for representing distributions, https://pypi.org/project/empiricaldist/. .
Although these are commonly used packages, they are not included with all Python installations, and they can be hard to install in some environments. If you have trouble installing them, I recommend using Anaconda or one of the other Python distributions that include these packages.
If you have a suggestion or correction, please send email to email@example.com. If I make a change based on your feedback, I will add you to the contributor list (unless you ask to be omitted).
If you include at least part of the sentence the error appears in, that makes it easy for me to search. Page and section numbers are fine, too, but not as easy to work with. Thanks!
First, I have to acknowledge David MacKay’s excellent book, Information Theory, Inference, and Learning Algorithms, which is where I first came to understand Bayesian methods. With his permission, I use several problems from his book as examples.
Several examples and exercises in the second edition are borrowed, with permission, from Cameron Davidson-Pilon and one exercise from Rasmus Bååth.
This book also benefited from my interactions with Sanjoy Mahajan, especially in Fall 2012, when I audited his class on Bayesian Inference at Olin College.
Many examples in this book were developed in collaboration with students in my Bayesian Statistics classes at Olin College. In particular, the Red Line example started as a class project by Brendan Ritter and Kai Austin.
I wrote parts of this book during project nights with the Boston Python User Group, so I would like to thank them for their company and pizza.
Jasmine Kwityn and Dan Fauxsmith at O’Reilly Media proofread the first edition and found many opportunities for improvement.
Linda Pescatore found a typo and made some helpful suggestions.
Tomasz Miasko sent many excellent corrections and suggestions.
For the second edition, I want to thank Michele Cronin and Kristen Brown at O’Reilly Media and the technical reviewers Ravin Kumar, Thomas Nield, Josh Starmer, and Junpeng Lao.
I am grateful to the developers and contributors of the software libraries this book is based on, especially Jupyter, NumPy, SciPy, Pandas, PyMC, ArviZ, and Matplotlib.
Other people who spotted typos and errors include Greg Marra, Matt Aasted, Marcus Ogren, Tom Pollard, Paul A. Giannaros, Jonathan Edwards, George Purkins, Robert Marcus, Ram Limbu, James Lawry, Ben Kahle, Jeffrey Law, Alvaro Sanchez, Olivier Yiptong, Yuriy Pasichnyk, Kristopher Overholt, Max Hailperin, Markus Dobler, Brad Minch, Allen Minch, Nathan Yee, Michael Mera, Chris Krenn, Daniel Vianna.