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  • BTU Cottbus  (6)
  • SB Kyritz
  • SB Rathenow
  • SB Ludwigsfelde
  • HS Musik Hanns Eisler
  • SB Ruhland
  • Stochastischer Prozess  (6)
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  • BTU Cottbus  (6)
  • SB Kyritz
  • SB Rathenow
  • SB Ludwigsfelde
  • HS Musik Hanns Eisler
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  • 1
    Online Resource
    Online Resource
    New York, NY : Springer New York
    UID:
    b3kat_BV042420830
    Format: 1 Online-Ressource (XIV, 289p. 15 illus)
    ISBN: 9781461397427 , 9781461397441
    Series Statement: Springer Series in Statistics, Probability and its Applications A Series of the Applied Probability Trust
    Note: This text on stochastic processes and their applications is based on a set of lectures given during the past several years at the University of California, Santa Barbara (UCSB). It is an introductory graduate course designed for classroom purposes. Its objective is to provide graduate students of statistics with an overview of some basic methods and techniques in the theory of stochastic processes. The only prerequisites are some rudiments of measure and integration theory and an intermediate course in probability theory. There are more than 50 examples and applications and 243 problems and complements which appear at the end of each chapter. The book consists of 10 chapters. Basic concepts and definitions are pro­ vided in Chapter 1. This chapter also contains a number of motivating ex­ amples and applications illustrating the practical use of the concepts. The last five sections are devoted to topics such as separability, continuity, and measurability of random processes, which are discussed in some detail. The concept of a simple point process on R+ is introduced in Chapter 2. Using the coupling inequality and Le Cam's lemma, it is shown that if its counting function is stochastically continuous and has independent increments, the point process is Poisson. When the counting function is Markovian, the sequence of arrival times is also a Markov process. Some related topics such as independent thinning and marked point processes are also discussed. In the final section, an application of these results to flood modeling is presented
    Language: English
    Keywords: Stochastischer Prozess
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  • 2
    Online Resource
    Online Resource
    New York, NY : Springer New York
    UID:
    b3kat_BV042421303
    Format: 1 Online-Ressource (IX, 199 p)
    ISBN: 9781475719925 , 9781475719949
    Series Statement: Applied Probability, A Series of the Applied Probability Trust 5
    Note: My first encounter with renewal theory and its extensions was in 1967/68 when I took a course in probability theory and stochastic processes, where the then recent book Stochastic Processes by Professor N.D. Prabhu was one of the requirements. Later, my teacher, Professor Carl-Gustav Esseen, gave me some problems in this area for a possible thesis, the result of which was Gut (1974a). Over the years I have, on and off, continued research in this field. During this time it has become clear that many limit theorems can be obtained with the aid of limit theorems for random walks indexed by families of positive, integer valued random variables, typically by families of stopping times. During the spring semester of 1984 Professor Prabhu visited Uppsala and very soon got me started on a book focusing on this aspect. I wish to thank him for getting me into this project, for his advice and suggestions, as well as his kindness and hospitality during my stay at Cornell in the spring of 1985. Throughout the writing of this book I have had immense help and support from Svante Janson. He has not only read, but scrutinized, every word and every formula of this and earlier versions of the manuscript. My gratitude to him for all the errors he found, for his perspicacious suggestions and remarks and, above all, for what his unusual personal as well as scientific generosity has meant to me cannot be expressed in words
    Language: English
    Keywords: Irrfahrtsproblem ; Grenzwertsatz ; Stochastischer Prozess ; Grenzwertsatz
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  • 3
    Online Resource
    Online Resource
    New York, NY : Springer New York
    UID:
    b3kat_BV042419742
    Format: 1 Online-Ressource (120p)
    ISBN: 9781461210948 , 9780387961972
    Series Statement: Lecture Notes in Statistics 30
    Note: About fifteen years ago Henning Rodhe and I disscussed the calculation of residence times, or lifetimes, of certain air pollutants for the first time. He was interested in pollutants which were mainly removed from the atmosphere by precipitation scavenging. His idea was to base the calculation on statistical models for the variation of the precipitation i~tensity and not only on the average precipitation intensity. In order to illustrate the importance of taking the variation into account we considered a simple model - here called the Markov model - for the precipitation intensity and computed the distribution of the residence time of an aerosol particle. Our expression for the average residence time - here formula (13- was rather much used by meteorologists. Certainly we were pleased, but while our ambition had been to provide an illustration, our work was merely understood as a proposal for a realistic model. Therefore we found it natural to search for more general models. The mathematical problems involved were the origin of my interest in this field. A brief outline of the background, purpose and content of this paper is given in section 1. It is a pleasure to thank Gunnar Englund, Georg Lindgren, Henning Rodhe and Michael Stein for their substantial help in the pre­ paration of this paper and Iren Patricius for her assistance in typing
    Language: English
    Keywords: Luftverschmutzung ; Konzentration ; Stochastisches Modell ; Schadstoffkonzentration ; Luftverschmutzung ; Stochastisches Modell ; Luftverschmutzung ; Stochastischer Prozess
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  • 4
    Online Resource
    Online Resource
    New York, NY : Springer New York
    UID:
    b3kat_BV042420073
    Format: 1 Online-Ressource (XII, 205p. 19 illus)
    ISBN: 9781461227267 , 9781461276432
    Series Statement: Springer Texts in Statistics
    Note: 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
    Language: English
    Keywords: Stochastischer Prozess ; Wahrscheinlichkeitstheorie ; Wahrscheinlichkeitsrechnung ; Einführung
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  • 5
    Online Resource
    Online Resource
    New York, NY : Springer New York
    UID:
    b3kat_BV042419113
    Format: 1 Online-Ressource (X, 322 p)
    ISBN: 9780387227658 , 9780387949819
    Series Statement: Springer Series in Statistics
    Note: Exponential families of stochastic processes are parametric stochastic p- cess models for which the likelihood function exists at all ?nite times and has an exponential representation where the dimension of the canonical statistic is ?nite and independent of time. This de?nition not only covers manypracticallyimportantstochasticprocessmodels,italsogivesrisetoa rather rich theory. This book aims at showing both aspects of exponential families of stochastic processes. Exponential families of stochastic processes are tractable from an a- lytical as well as a probabilistic point of view. Therefore, and because the theory covers many important models, they form a good starting point for an investigation of the statistics of stochastic processes and cast interesting light on basic inference problems for stochastic processes. Exponential models play a central role in classical statistical theory for independent observations, where it has often turned out to be informative and advantageous to view statistical problems from the general perspective of exponential families rather than studying individually speci?c expon- tial families of probability distributions. The same is true of stochastic process models. Thus several published results on the statistics of parti- lar process models can be presented in a uni?ed way within the framework of exponential families of stochastic processes
    Language: English
    Keywords: Stochastischer Prozess ; Exponentialfamilie
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  • 6
    Online Resource
    Online Resource
    New York ; Berlin ; Heidelberg ; London ; Paris ; Tokyo : Springer
    UID:
    b3kat_BV042421031
    Format: 1 Online-Ressource (xxiii, 470 Seiten)
    ISBN: 9781468403022
    Series Statement: Graduate Texts in Mathematics 113
    Note: Two of the most fundamental concepts in the theory of stochastic processes are the Markov property and the martingale property. * This book is written for readers who are acquainted with both of these ideas in the discrete-time setting, and who now wish to explore stochastic processes in their continuous­time context. It has been our goal to write a systematic and thorough exposition of this subject, leading in many instances to the frontiers of knowledge. At the same time, we have endeavored to keep the mathematical prerequisites as low as possible, namely, knowledge of measure-theoretic probability and some familiarity with discrete-time processes. The vehicle we have chosen for this task is Brownian motion, which we present as the canonical example of both a Markov process and a martingale. We support this point of view by showing how, by means of stochastic integration and random time change, all continuous-path martingales and a multitude of continuous-path Markov processes can be represented in terms of Brownian motion. This approach forces us to leave aside those processes which do not have continuous paths. Thus, the Poisson process is not a primary object of study, although it is developed in Chapter 1 to be used as a tool when we later study passage times and local time of Brownian motion
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-4684-0304-6
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 3-540-96535-1
    Language: English
    Subjects: Economics , Mathematics
    RVK:
    RVK:
    Keywords: Brownsche Bewegung ; Stochastischer Prozess ; Brownsche Bewegung ; Stochastische Analysis ; Brownsche Bewegung ; Stetigkeit ; Brownsche Bewegung ; Stochastik
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