Essential Mathematics and Statistics for Science by Graham Currell; Antony DowmanThis invaluable text allows science students to extend necessary skills and techniques, with the topics being developed through examples in science which are easily understood by students from a range of disciplines. The introductory approach eases students into the subject, progressing to cover topics relevant to first and second year study and support data analysis for final year projects.
Publication Date: 2009
A First Course in Abstract Algebra by Marlow Anderson; Todd FeilThis unique approach develops ring theory first & motivates students in the study of abstract algebra understanding the power of abstraction, and later on using examples of symmetries of figures in the plane and space as well as permutations. It includes straightforward exercises to quickly verify facts, warm-up exercises that test fundamental comprehension, and regular exercises that consist of computational and supply-the-proof problems.
This book sheds new light on stochastic calculus, the branch of mathematics that is most widely applied in financial engineering and mathematical finance. The first book to introduce pathwise formulae for the stochastic integral, it provides a simple but rigorous treatment of the subject, including a range of advanced topics, and also discusses in-depth topics such as quadratic variation, Ito formula, and Emery topology.
Introductory Mathematics & Statistics by Croucher, John S.Croucher demonstrates the relevance of mathematics and statistics in making decisions in day-to-day commercial situations. Numerous contemporary examples and pictorial presentations of data are provided to illustrate real world application of statistics.
This book elucidates the basic concepts and applications of operations research. Written in a lucid, well-structured and easy-to-understand language, the key topics are explained with adequate depth and self-explanatory flow charts. A wide range of solved examples and end-of-chapter exercises makes this book an ideal companion for active learners.
This practical, applications-oriented text demonstrates the key role of mathematics in optimization and linear systems. It explains effective procedures for performing mathematical tasks that arise in many fields, including operations research, engineering, systems sciences, statistics, and economics. Readers will learn how to resolve linear independence and find null spaces and factors of matrices, determine existence of restricted solutions to linear equations and inequalities, and resolve definiteness of Hermitian and real symmetric matrices by Gaussian pivoting. Additional topics include how to diagonalize -- or "nearly" diagonalize -- square matrices, differentiate vectors and matrices by the chain rule, solve systems of differential and difference equations, and other subjects. Most of the examples and many of the 1,300 problems illustrate techniques, and nearly all of the tables display reference material for procedures. Differential and integral calculus are prerequisites.
These lectures emphasized specific areas of operations research and the mathematics used in modeling and solving the related problems. Each lecturer attempted to make his presentation self-contained in terms of defining the application areas and mathematics employed.
The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject.
Introduction to Operations Research by A. Kaufmann; R. Faure (Editor)
The book provides a unified treatment of optimization methods. It brings ideas from mathematical programming (MP), constraint programming (CP), and global optimization (GO)into a single volume. There is no reason these must be learned as separate fields, as they normally are, and there are three reasons they should be studied together.
The book presents a comprehensive study of stochastic linear optimization problems and their applications. ... The presentation includes geometric interpretation, linear programming duality, and the simplex method in its primal and dual forms. ... The authors have made an effort to collect ... the most useful recent ideas and algorithms in this area. ... A guide to the existing software is included as well.
Decision-making is an important task no matter the industry. Operations research, as a discipline, helps alleviate decision-making problems through the extraction of reliable information related to the task at hand in order to come to a viable solution. Integrating stochastic processes into operations research and management can further aid in the decision-making process for industrial and management problems. Stochastic Processes and Models in Operations Research emphasizes mathematical tools and equations relevant for solving complex problems within business and industrial settings. This research-based publication aims to assist scholars, researchers, operations managers, and graduate-level students by providing comprehensive exposure to the concepts, trends, and technologies relevant to stochastic process modeling to solve operations research problems.
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 Biology and Finance offers a comprehensive examination to the most important issues of stochastic differential equations and their applications. Includes illustrative examples in modelling dynamical phenomena subject to randomness, mainly in biology, bioeconomics and finance, that clearly demonstrate the usefulness of stochastic differential equations in these and many other areas of science and technology. The text also features real-life situations with experimental data, thus covering topics such as Monte Carlo simulation and statistical issues of estimation, model choice and prediction. The book includes the basic theory of option pricing and its effective application using real-life.
This clear presentation of the most fundamental models of random phenomena employs methods that recognize computer-related aspects of theory. The text emphasizes the modern viewpoint, in which the primary concern is the behavior of sample paths. By employing matrix algebra and recursive methods, rather than transform methods, it provides techniques readily adaptable to computing with machines. Topics include probability spaces and random variables, expectations and independence, Bernoulli processes and sums of independent random variables, Poisson processes, Markov chains and processes, and renewal theory.
Stochastic Numerical Methods introduces the numerical methods that use probability or stochastic concepts to analyze random processes. The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where some of the techniques have been used recently to numerically simulate different agent-based models. Examples included in the book range from phase-transitions and critical phenomena, including details of data analysis (extraction of critical exponents, finite-size effects, etc.), to population dynamics, interfacial growth, chemical reactions, etc. Program listings are integrated in the discussion of numerical algorithms to facilitate their understanding.
An introduction to stochastic processes tfor students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader's understanding.