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Indigo An Introduction to Stochastic Modeling | Erin Mills Town Centre
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An Introduction to Stochastic Modeling , Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes for students familiar with elementary probability theory and calculus. The objectives 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 an integrated treatment of theory, applications and practical implementation. A well-regarded resource for many years, the text is an ideal foundation for a broad range of students.
Explores realistic applications from a variety of disciplines, including biological, chemical, physical, engineering, and financial examples
Presents a completely new treatment of modeling with stochastic differential equations, and expanded coverage of Brownian motion and martingale processes
New applications of Markov chains to the simulation of chemical reactions via the Gillespie algorithm and to Bayesian inference via the Metropolis-Hastings algorithm
Provides extensive end-of-section exercises sets with answers, as well as numerical illustrations
Each chapter concludes with a section focusing on computational examples, code, and exercises that will empower students to explore concepts in a practical way
Offers online support, sample code and solutions to coding problems for instructors, and electronic access to sample Python code for students
An Introduction to Stochastic Modeling , Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes for students familiar with elementary probability theory and calculus. The objectives 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 an integrated treatment of theory, applications and practical implementation. A well-regarded resource for many years, the text is an ideal foundation for a broad range of students.
Explores realistic applications from a variety of disciplines, including biological, chemical, physical, engineering, and financial examples
Presents a completely new treatment of modeling with stochastic differential equations, and expanded coverage of Brownian motion and martingale processes
New applications of Markov chains to the simulation of chemical reactions via the Gillespie algorithm and to Bayesian inference via the Metropolis-Hastings algorithm
Provides extensive end-of-section exercises sets with answers, as well as numerical illustrations
Each chapter concludes with a section focusing on computational examples, code, and exercises that will empower students to explore concepts in a practical way
Offers online support, sample code and solutions to coding problems for instructors, and electronic access to sample Python code for students
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