In recent years, random variables and stochastic processes have emerged as important factors in predicting outcomes in virtually every field of applied and social science. Ironically, according to Nicolas Bouleau and Dominique Lepingle, the presence of randomness in the model sometimes leads engineers to accept crude mathematical treatments that produce inaccurate results. The purpose of Numerical Methods for Stochastic Processes is to add greater rigor to numerical treatment of stochastic processes so that they produce results that can be relied upon when making decisions and assessing risks. Based on a postgraduate course given by the authors at Paris 6 University, the text emphasizes simulation methods, which can now be implemented with specialized computer programs. Specifically presented are the Monte Carlo and shift methods, which use an "imitation of randomness" and have a wide range of applications, and the so-called quasi-Monte Carlo methods, which are rigorous but less widely applicable. Offering a broad introduction to the field, this book presents the current state of the main methods and ideas and the cases for which they have been proved. Nevertheless, the authors do explore problems raised by these newer methods and suggest areas in which further research is needed. Extensive notes and a full bibliography give interested readers the option of delving deeper into stochastic numerical analysis. For professional statisticians, engineers, and physical and social scientists, Numerical Methods for Stochastic Processes provides both the theoretical background and the necessary practical tools to improve predictions based on randomness in the model. With its exercises andbroad-spectrum coverage, it is also an excellent textbook for introductory graduate-level courses in stochastic process mathematics.
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The application of number-theoretic methods is a new, but rapidly expanding, branch of statistics. The Monte Carlo method is already established, with wide applications in science and technology. In applying it, however, a set of 'pseudo' random numbers is required for statistical simulation, and the use of these numbers often leads to unacceptably large errors. The essence of the number-theoretic method described in this book is to reduce such errors by using number theory to find a set of points (sometimes called quasi random numbers) which can then be regarded as the representatives of a given distribution. The number-theoretic method is hence also known as the quasi or deterministic version of the Monte Carlo method. Number-theoretic Methods in Statistics gives the reader various methods of generating quasi random numbers and demonstrates their applications in solving a variety of statistical problems, for example, the numerical evaluation of probabilities and moments, optimization, experimental design including design of computer experiments and statistical inference.
In the analysis and the quest for an understanding of a physical system, generally, the formulation and use of a mathematical model that is thought to describe the system is an essential step. That is, a mathematical model is formulated (as a system of equations) which is thought to quantitatively define the interrelationships between phenomena that define the characteristics of the physical system. The mathematical model is usually tested against observations of the physical system, and if the agreement is considered acceptable, the model is then taken as a representation of the physical system, at least until improvements in the observations lead to refinements and extensions of the model. Often the model serves as a guide to new observations. Ideally, this process of refinement of the observations and model leads to improvements of the model and thus enhanced understanding of the physical system. However, this process of comparing observations with a proposed model is not possible until the model equations are solved to give a solution that is then the basis for the comparison with observations. The solution of the model equations is often a challenge. Typically in science and engineering this involves the integration of systems of ordinary and partial differential equations (ODE/PDEs). The intent of this volume is to assist scientists and engineers in this process of solving differential equation models by explaining some numerical, computer-based methods that have generally been proven to be effective for the solution of a spectrum of ODE/PDE system problems. For PDE models, we have focused on the method of lines (MOL), a well established numerical procedure in which the PDE spatial (boundary value) partial derivatives are approximated algebraically, in our case, by finite differences (FDs). The resulting differential equations have only one independent variable remaining, an initial value variable, typically time in a physical application. Thus, the MOL approximation replaces a PDE system with an initial value ODE system. This ODE system is then integrated using a standard routine, which for the Matlab analysis used in the example applications, is one of the Matlab library integrators. In this way, we can take advantage of the recent progress in ODE numerical integrators. However, whilst we have presented our MOL solutions in terms of Matlab code, it is not our intention to provide optimised Matlab code but, rather, to provide code that will be readily understood and that can be converted easily to other computer languages. This approach has been adopted in view of our experience that there is considerable interest in numerical solutions written in other computer languages such as Fortran, C, C++, Java, etc. Nevertheless, discussion of specific Matlab proprietary routines is included where this is thought to be of benefit to the reader. Important variations on the MOL are possible. For example, the PDE spatial derivatives can be approximated by finite elements, finite volumes, weighted residual methods and spectral methods. All of these approaches have been used and are described in the numerical analysis literature. For our purposes, and to keep the discussion to a reasonable length, we have focused on FDs. Specifically, we provide library routines for FDs of orders two to ten.
Inequality has become an essential tool in many areas of mathematical research, for example in probability and statistics where it is frequently used in the proofs. "Probability Inequalities" covers inequalities related with events, distribution functions, characteristic functions, moments and random variables (elements) and their sum. The book shall serve as a useful tool and reference for scientists in the areas of probability and statistics, and applied mathematics. Prof. Zhengyan Lin is a fellow of the Institute of Mathematical Statistics and currently a professor at Zhejiang University, Hangzhou, China. He is the prize winner of National Natural Science Award of China in 1997. Prof. Zhidong Bai is a fellow of TWAS and the Institute of Mathematical Statistics; he is a professor at the National University of Singapore and Northeast Normal University, Changchun, China.
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Numerical methods for stochastic processes by Nicolas Bouleau, Dominique Lépingle | Literally Just Books
