An Introduction to Probability and Stochastic Processes [Springer Texts in Statistics]

Berger, Marc A.

ISBN 10: 0387977848 / ISBN 13: 9780387977843

Published by Springer-Verlag, New York, 1993

About this Item

8vo, hardcover. No dj, as issued. NEW. Bright, crisp & clean, unread; covers glossy. xii, 205 p.

Seller Inventory # 1150327.28

Bibliographic Details

• Title: An Introduction to Probability and Stochastic Processes [Springer Texts in Statistics]
• Publisher: Springer-Verlag, New York
• Publication Date: 1993
• Language: English
• ISBN 10: 0387977848
• ISBN 13: 9780387977843
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.