Allen Downey – författare

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 not many resources are 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 statisticsWork with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testingGet started with simple examples, using coins, dice, and a bowl of cookiesLearn computational methods for solving real-world problems

Want to learn how to program and think like a computer scientist? This practical guide gets you started on your programming journey with the help of Perl 6, the younger sister of the popular Perl programming language. Ideal for beginners, this hands-on book includes over 100 exercises with multiple solutions, and more than 1,000 code examples so you can quickly practice what you learn. Experienced programmers-especially those who know Perl 5-will also benefit. Divided into two parts, Think Perl 6 starts with basic concepts that every programmer needs to know, and then focuses on different programming paradigms and some more advanced programming techniques. With two semesters' worth of lessons, this book is the perfect teaching tool for computer science beginners in colleges and universities.Learn basic concepts including variables, expressions, statements, functions, conditionals, recursion, and loops Understand commonly used basic data structures and the most useful algorithms Dive into object-oriented programming, and learn how to construct your own types and methods to extend the language Use grammars and regular expressions to analyze textual content Explore how functional programming can help you make your code simpler and more expressive

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.

Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.

In this updated second edition, you will:

Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier TransformStudy abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machinesGet Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automataExplore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism

Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.

Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.

In this updated second edition, you will:

Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier TransformStudy abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machinesGet Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automataExplore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism

Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modelling - that is, the art of describing and simulating real-world systems. Readers are guided through modelling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.