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Generalized Stochastic Processes
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Book Synopsis Generalized Stochastic Processes by : Stefan Schäffler
Download or read book Generalized Stochastic Processes written by Stefan Schäffler and published by Springer. This book was released on 2018-06-21 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook shall serve a double purpose: first of all, it is a book about generalized stochastic processes, a very important but highly neglected part of probability theory which plays an outstanding role in noise modelling. Secondly, this textbook is a guide to noise modelling for mathematicians and engineers to foster the interdisciplinary discussion between mathematicians (to provide effective noise models) and engineers (to be familiar with the mathematical backround of noise modelling in order to handle noise models in an optimal way).Two appendices on "A Short Course in Probability Theory" and "Spectral Theory of Stochastic Processes" plus a well-choosen set of problems and solutions round this compact textbook off.
Book Synopsis Covariances of Generalized Stochastic Processes by : Lewis I. Pakula
Download or read book Covariances of Generalized Stochastic Processes written by Lewis I. Pakula and published by . This book was released on 1972 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Generalized Stochastic Processes by : A. J. E. M. Janssen
Download or read book Generalized Stochastic Processes written by A. J. E. M. Janssen and published by . This book was released on 1976 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Processes: General Theory by : Malempati M. Rao
Download or read book Stochastic Processes: General Theory written by Malempati M. Rao and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Processes: General Theory starts with the fundamental existence theorem of Kolmogorov, together with several of its extensions to stochastic processes. It treats the function theoretical aspects of processes and includes an extended account of martingales and their generalizations. Various compositions of (quasi- or semi-)martingales and their integrals are given. Here the Bochner boundedness principle plays a unifying role: a unique feature of the book. Applications to higher order stochastic differential equations and their special features are presented in detail. Stochastic processes in a manifold and multiparameter stochastic analysis are also discussed. Each of the seven chapters includes complements, exercises and extensive references: many avenues of research are suggested. The book is a completely revised and enlarged version of the author's Stochastic Processes and Integration (Noordhoff, 1979). The new title reflects the content and generality of the extensive amount of new material. Audience: Suitable as a text/reference for second year graduate classes and seminars. A knowledge of real analysis, including Lebesgue integration, is a prerequisite.
Book Synopsis Smoothing, Filtering and Prediction of Generalized Stochastic Processes by : León Abreu (José Luis)
Download or read book Smoothing, Filtering and Prediction of Generalized Stochastic Processes written by León Abreu (José Luis) and published by . This book was released on 1970 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Modelling with Generalized Stochastic Petri Nets by : M. Ajmone Marsan
Download or read book Modelling with Generalized Stochastic Petri Nets written by M. Ajmone Marsan and published by . This book was released on 1995-12-12 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: World renowned leaders in the field provide an accessible introduction to the use of Generalized Stochastic Petri Nets (GSPNs) for the performance analysis of diverse distributed systems. Divided into two parts, it begins with a summary of the major results in GSPN theory. The second section is devoted entirely to application examples which demonstrate how GSPN methodology can be used in different arenas. A simple version of the software tool used to analyse GSPN models is included with the book and a concise manual for its use is presented in the later chapters.
Book Synopsis Series Representations for Generalized Stochastic Processes by : Muhammad K. Habib
Download or read book Series Representations for Generalized Stochastic Processes written by Muhammad K. Habib and published by . This book was released on 1986 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the Connection Between Ordinary and Generalized Stochastic Processes by : R. Meidan
Download or read book On the Connection Between Ordinary and Generalized Stochastic Processes written by R. Meidan and published by . This book was released on 1977 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Orthogonal Decompositions for Generalized Stochastic Processes with Independent Values by : Suman Das
Download or read book Orthogonal Decompositions for Generalized Stochastic Processes with Independent Values written by Suman Das and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Introduction to Sparse Stochastic Processes by : Michael Unser
Download or read book An Introduction to Sparse Stochastic Processes written by Michael Unser and published by Cambridge University Press. This book was released on 2014-08-21 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed guide to sparsity, providing a description of their transform-domain statistics and applying the models to practical algorithms.
Book Synopsis Orthogonal Decompositions for Generalized Stochastic Processes with Independent Values by :
Download or read book Orthogonal Decompositions for Generalized Stochastic Processes with Independent Values written by and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Processes and Applications by : Grigorios A. Pavliotis
Download or read book Stochastic Processes and Applications written by Grigorios A. Pavliotis and published by Springer. This book was released on 2014-11-19 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents various results and techniques from the theory of stochastic processes that are useful in the study of stochastic problems in the natural sciences. The main focus is analytical methods, although numerical methods and statistical inference methodologies for studying diffusion processes are also presented. The goal is the development of techniques that are applicable to a wide variety of stochastic models that appear in physics, chemistry and other natural sciences. Applications such as stochastic resonance, Brownian motion in periodic potentials and Brownian motors are studied and the connection between diffusion processes and time-dependent statistical mechanics is elucidated. The book contains a large number of illustrations, examples, and exercises. It will be useful for graduate-level courses on stochastic processes for students in applied mathematics, physics and engineering. Many of the topics covered in this book (reversible diffusions, convergence to equilibrium for diffusion processes, inference methods for stochastic differential equations, derivation of the generalized Langevin equation, exit time problems) cannot be easily found in textbook form and will be useful to both researchers and students interested in the applications of stochastic processes.
Book Synopsis Reproducing-kernel Hilbert Spaces of Distributions, and Generalized Stochastic Processes by : R. Meidan
Download or read book Reproducing-kernel Hilbert Spaces of Distributions, and Generalized Stochastic Processes written by R. Meidan and published by . This book was released on 1976 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Generalized Stochastic Process for Count Data by : Li Zhu
Download or read book A Generalized Stochastic Process for Count Data written by Li Zhu and published by . This book was released on 2014 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Bernoulli and Poisson are two popular discrete count processes; however, both rely on strict assumptions that motivate their use. We instead propose a generalized count process (the Conway-Maxwell-Poisson process) that not only includes the Bernoulli and Poisson processes as special cases, but also serves as a flexible mechanism to describe count processes that approximate data with over- and under-dispersion. We introduce the process and its associated generalized waiting time distribution with several real-data applications to illustrate its flexibility for a variety of data structures.
Book Synopsis Series Expansions of Generalized Stochastic Processes by : R. Meidan
Download or read book Series Expansions of Generalized Stochastic Processes written by R. Meidan and published by . This book was released on 1976 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the Martingale Property for Generalized Stochastic Processes by : Fred Espen Benth
Download or read book On the Martingale Property for Generalized Stochastic Processes written by Fred Espen Benth and published by . This book was released on 1995 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Cauchy Problems in Infinite Dimensions by : Irina V. Melnikova
Download or read book Stochastic Cauchy Problems in Infinite Dimensions written by Irina V. Melnikova and published by CRC Press. This book was released on 2016-04-27 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic Cauchy Problems in Infinite Dimensions: Generalized and Regularized Solutions presents stochastic differential equations for random processes with values in Hilbert spaces. Accessible to non-specialists, the book explores how modern semi-group and distribution methods relate to the methods of infinite-dimensional stochastic analysis. It also shows how the idea of regularization in a broad sense pervades all these methods and is useful for numerical realization and applications of the theory. The book presents generalized solutions to the Cauchy problem in its initial form with white noise processes in spaces of distributions. It also covers the "classical" approach to stochastic problems involving the solution of corresponding integral equations. The first part of the text gives a self-contained introduction to modern semi-group and abstract distribution methods for solving the homogeneous (deterministic) Cauchy problem. In the second part, the author solves stochastic problems using semi-group and distribution methods as well as the methods of infinite-dimensional stochastic analysis.
