Probability, Stochastic Process, Queueing Theory, etc.

This book is an introduction to probability and mathematical statistics intended for students already having some elementary mathematical background.

The three main themes of this book, probability theory, differential geometry, and the theory of integrable systems, reflect the broad range of mathematical interests of Henry McKean, to whom it is dedicated. Written by experts in probability, geometry, integrable systems, turbulence, and percolation, the seventeen papers included here demonstrate a wide variety of techniques that have been developed to solve various mathematical problems in these areas. The topics are often combined in an unusual and interesting fashion to give solutions outside of the standard methods. The papers contain some exciting results and offer a guide to the contemporary literature on these subjects.

Stochastic Calculus for Finance evolved from the first ten years of the Carnegie Mellon Professional Master program in Computational Finance. The content of this book has been used successfully with students whose mathematics background consists of calculus and calculus-based probability.

This is a collection of lectures on probability theory and mathematical statistics written by Marco Taboga, a professional financial economist with a passion for mathematics. It is offered as a free service to the mathematical community and provides an accessible introduction to topics that are not usually found in elementary textbooks. It collects results and proofs, especially on probability distributions, that are hard to find in standard references and are scattered here and there in more specialistic books.

This book presents an introduction to probability and mathematical statistics and it is intended for students already having some elementary mathematical background.

This book brings a modern flavor to the course, incorporating the computer and offering an integrated approach to inference that includes the frequency approach and the Bayesian inference.

This book introduces modern concepts of applied Stochastic Processes is written for a broad range of applications in diverse areas of engineering and the sciences. Emphasis is on clarifying the basic principles supporting current prediction techniques.

This comprehensive guide to Stochastic Processes gives a complete overview of the theory and addresses the most important applications. Pitched at a level accessible to beginning graduate students and researchers from applied disciplines, it is both a course book and a rich resource for individual readers.

This guide to Stochastic Processes covers a wide range of topics. Short, readable chapters aim for clarity rather than full generality. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems.

Well-written and accessible, this classic introduction to Stochastic Processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory.

This is a 'first course' in the sense that it presumes no previous course in probability. The units are numbered as they appear in the text, although of course they may be used in any desired order. For those who wish to use the order of the text, an outline is provided, with indication of which modules contain the material.

This introduction to more advanced courses in probability and real analysis emphasizes the probabilistic way of thinking, rather than measure-theoretic concepts.

The goal of this book is to give useful understanding for solving problems formulated by stochastic differential equations models in science, engineering and mathematical finance. Typically, these problems require numerical methods to obtain a solution and therefore the course focuses on basic understanding of stochastic and partial differential equations to construct reliable and efficient computational methods.

This open access book provides a comprehensive introduction to the theory of stochastic partial differential equations (SPDEs). The focus is on SPDEs driven by Gaussian space-time white noise. The book covers both linear and nonlinear SPDEs, with Lipschitz and locally Lipschitz coefficients and multiplicative noise.

This book provides an introduction to probability theory and discrete and continuous Stochastic Processes and its applications. It has a unique approach that provides a broad and wide introduction into the fascinating area of probability theory.