Publisher's Synopsis
It is well understood these days that design is not a one-step process, but that it progresses along many phases which, starting from an initial idea, include drafting, preliminary evaluations, trial and error procedures, verifications and all that. Stochastic modeling is a technique of presenting data or predicting outcomes that takes into account a certain degree of randomness, or unpredictability. Due to actual spread of fast and inexpensive computational power everywhere in the world, the best approach is to model a real phenomenon as faithfully as possible, and then rely on a simulation study to analyze it. The insurance industry, for example, depends greatly on stochastic modeling for predicting the future condition of company balance sheets, since these may depend on unpredictable events resulting in the paying of claims. Many other industries and fields of study can benefit from stochastic modeling, such as statistics, stock investing, biology, linguistics, and quantum physics. Another real-world application of stochastic modeling, besides insurance, is manufacturing. Manufacturing is seen as a stochastic process because of the effect that unknown or random variables can have on the end result. For example, a factory which makes a certain product will always find that a small percentage of the products do not come out as intended, and cannot be sold. This may be due to a variety of factors, such as the quality of inputs, the working condition of the production machinery, and the competence of employees, among others. This volume provides an assortment of outstanding investigations in various aspects of stochastic systems and their behavior. The notion of stochastic processes is very important both in mathematical theory and its applications in science, engineering, economics, etc. It is used to model a large number of numerous phenomena where the quantity of interest varies individually or continuously through time in a non-predictable fashion. This work provides a self-reliant treatment on real-world aspects of stochastic modeling and simulation including applications drawn from engineering, statistics, and computer science. By outlining the new approaches and modern methods of simulation of stochastic processes, this volume will be of interest to students and researchers dealing with stochastic processes.
