An Introduction To Stochastic Modeling 4th Edition

An Introduction to Stochastic Modeling

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Fourth Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. 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. New to this edition: - Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications - Plentiful, completely updated problems - Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers - New chapters of stochastic differential equations and Brownian motion and related processes - Additional sections on Martingale and Poisson process - Realistic applications from a variety of disciplines integrated throughout the text - Extensive end of chapter exercises sets, 250 with answers - Chapter 1-9 of the new edition are identical to the previous edition - New! Chapter 10 - Random Evolutions - New! Chapter 11- Characteristic functions and Their Applications

Stochastic Approaches for Systems Biology

This textbook focuses on stochastic analysis in systems biology containing both the theory and application. While the authors provide a review of probability and random variables, subsequent notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of stochastic framework for modeling subcellular biochemical systems. In particular, the authors make an effort to show how the notion of propensity, the chemical master equation and the stochastic simulation algorithm arise as consequences of the Markov property. The text contains many illustrations, examples and exercises to illustrate the ideas and methods that are introduced. Matlab code is also provided where appropriate. Additionally, the cell cycle is introduced as a more complex case study. Senior undergraduate and graduate students in mathematics and physics as well as researchers working in the area of systems biology, bioinformatics and related areas will find this text useful.

Stochastic Modeling and Geostatistics