Modelling and Application of Stochastic Processes:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boston, MA
Springer US
1986
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| Subjects: | |
| Online Access: | Volltext |
| Item Description: | The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realization problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically efficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side, chapters on use of Markov random fields for modelling and analyzing image signals, use of complementary models for the smoothing problem with missing data, and nonlinear estimation are included. Chapter 1 by Klein and Dickinson develops the nested orthogonal state space realization for ARMA processes. As suggested by the name, nested orthogonal realizations possess two key properties; (i) the state variables are orthogonal, and (ii) the system matrices for the (n + 1)st order realization contain as their "upper" n-th order blocks the system matrices from the n-th order realization (nesting property) |
| Physical Description: | 1 Online-Ressource (XIV, 288 p) |
| ISBN: | 9781461322672 9781461294009 |
| DOI: | 10.1007/978-1-4613-2267-2 |
Staff View
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| 500 | |a The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realization problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically efficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side, chapters on use of Markov random fields for modelling and analyzing image signals, use of complementary models for the smoothing problem with missing data, and nonlinear estimation are included. Chapter 1 by Klein and Dickinson develops the nested orthogonal state space realization for ARMA processes. As suggested by the name, nested orthogonal realizations possess two key properties; (i) the state variables are orthogonal, and (ii) the system matrices for the (n + 1)st order realization contain as their "upper" n-th order blocks the system matrices from the n-th order realization (nesting property) | ||
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| dewey-hundreds | 500 - Natural sciences and mathematics |
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| indexdate | 2024-07-10T01:20:48Z |
| institution | BVB |
| isbn | 9781461322672 9781461294009 |
| language | English |
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| spelling | Desai, Uday B. Verfasser aut Modelling and Application of Stochastic Processes edited by Uday B. Desai Boston, MA Springer US 1986 1 Online-Ressource (XIV, 288 p) txt rdacontent c rdamedia cr rdacarrier The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realization problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically efficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side, chapters on use of Markov random fields for modelling and analyzing image signals, use of complementary models for the smoothing problem with missing data, and nonlinear estimation are included. Chapter 1 by Klein and Dickinson develops the nested orthogonal state space realization for ARMA processes. As suggested by the name, nested orthogonal realizations possess two key properties; (i) the state variables are orthogonal, and (ii) the system matrices for the (n + 1)st order realization contain as their "upper" n-th order blocks the system matrices from the n-th order realization (nesting property) Physics Distribution (Probability theory) Acoustics Computer engineering Signal, Image and Speech Processing Electrical Engineering Probability Theory and Stochastic Processes Stochastischer Prozess (DE-588)4057630-9 gnd rswk-swf Stochastischer Prozess (DE-588)4057630-9 s 1\p DE-604 https://doi.org/10.1007/978-1-4613-2267-2 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Desai, Uday B. Modelling and Application of Stochastic Processes Physics Distribution (Probability theory) Acoustics Computer engineering Signal, Image and Speech Processing Electrical Engineering Probability Theory and Stochastic Processes Stochastischer Prozess (DE-588)4057630-9 gnd |
| subject_GND | (DE-588)4057630-9 |
| title | Modelling and Application of Stochastic Processes |
| title_auth | Modelling and Application of Stochastic Processes |
| title_exact_search | Modelling and Application of Stochastic Processes |
| title_full | Modelling and Application of Stochastic Processes edited by Uday B. Desai |
| title_fullStr | Modelling and Application of Stochastic Processes edited by Uday B. Desai |
| title_full_unstemmed | Modelling and Application of Stochastic Processes edited by Uday B. Desai |
| title_short | Modelling and Application of Stochastic Processes |
| title_sort | modelling and application of stochastic processes |
| topic | Physics Distribution (Probability theory) Acoustics Computer engineering Signal, Image and Speech Processing Electrical Engineering Probability Theory and Stochastic Processes Stochastischer Prozess (DE-588)4057630-9 gnd |
| topic_facet | Physics Distribution (Probability theory) Acoustics Computer engineering Signal, Image and Speech Processing Electrical Engineering Probability Theory and Stochastic Processes Stochastischer Prozess |
| url | https://doi.org/10.1007/978-1-4613-2267-2 |
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