Springer, 2020. — 157 p. — (Springer Undergraduate Texts in Mathematics and Technology). — ISBN: 3030560694.
This undergraduate textbook presents an
inquiry-based learning course in stochastic models and computing designed to serve as a first course in probability. Its modular structure complements a traditional lecture format, introducing new topics chapter by chapter with accompanying projects for group collaboration. The text addresses
probability axioms leading to Bayes’ theorem, discrete and continuous random variables, Markov chains, and Brownian motion, as well as applications including
randomized algorithms, randomized surveys, Benford’s law, and Monte Carlo methods. Adopting a unique application-driven approach to better study probability in action, the book emphasizes
data, simulation, and games to strengthen reader insight and intuition while proving theorems. Additionally, the text incorporates codes and exercises in the
Julia programming language to further promote a hands-on focus in modelling. Students should have prior knowledge of single variable calculus.
Note from the Preface:The book uses the
Julia programming language (version 1.1.0). The Julia software is free on a license from MIT. The simplicity of Julia allows incorporating the computer code within the main narrative, without a significant distraction to the main flow of the narrative.
A tutorial on Julia can be found in Chapter 1 of
"First Semester in Numerical Analysis with Julia", an
open-access textbook I wrote on numerical analysis, published by
Florida State University Libraries. For readers who might
prefer the
Python programming language, a companion book
"Probability and Simulation: A Python Companion" cowritten with Yaning Liu
is available on the Springer web page for the book. The companion book has the
Python translations of the Julia codes presented here.
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