Stochastic Processes Nus
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Seminar on Stochastic Processes, 1987
Author: Cinlar
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The 1987 Seminar on Stochastic Processes was held at Princeton University, March 26 through March 28, 1987. It was the seventh seminar in a continuing series of meetings which provide opportunities for researchers to discuss current work in stochastic processes in an informal and enjoyable atmosphere. Previous seminars were held at Northwestern University, Evanston; University of Florida, Gainesville: and University of Virginia, Charlottesville. The success of these seminars has been due to the interest and enthusiasm of probabilists in the United States and abroad. Many of the participants have allowed us to pUblish the results of their re search in this volume. The editors hope that the reader will be able to sense some of the excitement present in the seminar by reading these articles. This year's invited participants included M. Aizenman, B. Atkinson, R.M. Blumenthal, C. Burdzy, D. Burkholder, R. Carmona, K.L. Chung, M. Cranston, C. Dellacherie, J.D. Deuschel, N. Dinculeanu, Gundy, P. Hsu, E.B. Dynkin, P. Fitzsimmons, R.K. Getoor, J. Glover, R.G. Hunt, H. Kaspi, Knight, G. Lawler, P. March, P.A. Meyer, A.F.J. Mitro, J. Neveu, E. Pardoux, M. Pinsky, L. Pitt, A.O. Pittenger, Z. Pop-Stojanovic, P. Protter, M. Rao, T. Salisbury, M.J. Sharpe, S.J. Taylor, E. Toby, S.R.S. Varadhan, R. Williams, M. Weber, and Z. Zhao.
An Introduction to Probability and Stochastic Processes
Author: Marc A. Berger
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
These notes were written as a result of my having taught a "nonmeasure theoretic" course in probability and stochastic processes a few times at the Weizmann Institute in Israel. I have tried to follow two principles. The first is to prove things "probabilistically" whenever possible without recourse to other branches of mathematics and in a notation that is as "probabilistic" as possible. Thus, for example, the asymptotics of pn for large n, where P is a stochastic matrix, is developed in Section V by using passage probabilities and hitting times rather than, say, pulling in Perron Frobenius theory or spectral analysis. Similarly in Section II the joint normal distribution is studied through conditional expectation rather than quadratic forms. The second principle I have tried to follow is to only prove results in their simple forms and to try to eliminate any minor technical com putations from proofs, so as to expose the most important steps. Steps in proofs or derivations that involve algebra or basic calculus are not shown; only steps involving, say, the use of independence or a dominated convergence argument or an assumptjon in a theorem are displayed. For example, in proving inversion formulas for characteristic functions I omit steps involving evaluation of basic trigonometric integrals and display details only where use is made of Fubini's Theorem or the Dominated Convergence Theorem.
Computational Fluid and Solid Mechanics
The MIT mission - "to bring together Industry and Academia and to nurture the next generation in computational mechanics is of great importance to reach the new level of mathematical modeling and numerical solution and to provide an exciting research environment for the next generation in computational mechanics." Mathematical modeling and numerical solution is today firmly established in science and engineering. Research conducted in almost all branches of scientific investigations and the design of systems in practically all disciplines of engineering can not be pursued effectively without, frequently, intensive analysis based on numerical computations.The world we live in has been classified by the human mind, for descriptive and analysis purposes, to consist of fluids and solids, continua and molecules; and the analyses of fluids and solids at the continuum and molecular scales have traditionally been pursued separately. Fundamentally, however, there are only molecules and particles for any material that interact on the microscopic and macroscopic scales. Therefore, to unify the analysis of physical systems and to reach a deeper understanding of the behavior of nature in scientific investigations, and of the behavior of designs in engineering endeavors, a new level of analysis is necessary. This new level of mathematical modeling and numerical solution does not merely involve the analysis of a single medium but must encompass the solution of multi-physics problems involving fluids, solids, and their interactions, involving multi-scale phenomena from the molecular to the macroscopic scales, and must include uncertainties in the given data and the solution results. Nature does not distinguish between fluids and solids and does not ever repeat itself exactly.This new level of analysis must also include, in engineering, the effective optimization of systems, and the modeling and analysis of complete life spans of engineering products, from design to fabrication, to possibly multiple repairs, to end of service.