Introduction to Stochastic Programming

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specif

A textbook that uses a hands-on approach to teach principles of programming languages, with Java as the implementation language.This introductory textbook teaches the principles of programming languages by using an experiential learning style, in which students learn about language features by realizing those features in a pedagogical compiler. Students use Java―the most commonly used programming language in the first two years of the computer science curriculum―as the implementation language throughout. The book also discusses a range of emerging topics in programming languages missing from existing textbooks, including concurrency, Big Data, and event-driven programming. The goal is to prepare students to design, implement, analyze, and understand both domain-specific and general-purpose programming languages. The book first develops basic concepts in languages, including means of computation using primitive values, means of combination such as variable definition and functions, an

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The new edition of an introduction to the art of computational problem solving using Python.This book introduces students with little or no prior programming experience to the art of computational pro

A comprehensive introduction to the core issues of stochastic differential equations and their effective application Introduction to Stochastic Differential Equations with Applications to Modelling in

This text is intended for a 1-semester CS1 course sequence. The Brief Version contains the first 18 chapters of the Comprehensive Version. The first 13 chapters are appropriate for preparing the AP Co

Classroom-tested by tens of thousands of students, this new edition of the bestselling intro to programming book is for anyone who wants to understand computer science. Learn about design, algorithms,

This book provides a comprehensive introduction to the theory of stochastic calculus and some of its applications. It is the only textbook on the subject to include more than two hundred exercises wit

Guide beginning programmers through the fundamentals of the C# language using its most recent features with Farrell's MICROSOFT VISUAL C# 2017: AN INTRODUCTION TO OBJECT-ORIENTED PROGRAMMING, 7E. Appr

Guide beginning programmers through the fundamentals of the C# language using its most recent features with Farrell's MICROSOFT VISUAL C# 2017: AN INTRODUCTION TO OBJECT-ORIENTED PROGRAMMING, 7E. Appr

Providing an introduction to stochastic optimal control in in?nite dimension, this book gives a complete account of the theory of second-order HJB equations in in?nite-dimensional Hilbert spaces, focu

ALERT: Before you purchase, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist