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Designed for engineers, mathematicians, computer scientists, financial analysts, and anyone interested in using numerical linear algebra, matrix theory, and game... Læs mere
Readers must visit the HTML version of each chapter and access the Electronic Supplementary Material. Extras for Appendices A & B can be found in Extras for Chapter 18.
The conference examines the historical roots and expansive potential of computation and... Læs mere
Bayesian inference uses probability distributions and Bayes' theorem to build flexible models. The book uses PyMC3 to abstract all the mathematical and computational details from this process allowing readers to solve a wide range of problems in data science.
Discrete math deals with studying finite and distinct elements. With this... Læs mere
Work Automation with R provides a solid framework on how to automate tasks and workflows in the workplace with R. This book introduces the most common components used in R automation, one by one, and then shows how to combine them to tackle real-world tasks.
This textbook, based on the author’s course on linear modeling at UC Berkeley taught over the past ten years, only requires basic knowledge of linear algebra, probability... Læs mere
Explores the intricate workflows of applied Bayesian statistics, aiming to uncover the tacit knowledge often overlooked in published papers and textbooks. By systematizing the process... Læs mere