# Books

All of my books are available under a free license that allows anyone to make and distribute modified versions.  Some of my books (listed below) have been published by O’Reilly media.

Others, many of them works in progress, are available from Green Tea Press. ### Think Stats

Think Stats is an introduction to Probability and Statistics for Python programmers.

• Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
• If you have basic skills in Python, you can use them to learn concepts in probability and statistics.Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding. ### Think Python

Think Python is an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters.

Some examples and exercises are based on Swampy, a Python package written by the author to demonstrate aspects of software design, and to give readers a chance to experiment with simple graphics and animation. ### Think Bayes

Think Bayes is an introduction to Bayesian statistics using computational methods.

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 mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, 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 simple loops.

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 conventional analysis. Also, it provides a smooth development path from simple examples to real-world problems. ### Think Complexity

This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science:

• Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower.An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graph—nested dictionaries—and several graph algorithms that use this data structure.
• Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundamental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python.
• Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation.
• Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology.

This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.

Complexity science is an interdisciplinary field—at the intersection of mathematics, computer science and physics—that focuses on these kinds of models. That’s what this book is about.