Based on the author’s own research, this book rigorously and systematically develops the theory of Gaussian white noise measures on Hilbert spaces to provide a comprehensive account of nonlinear filtering theory. Covers Markov processes, cylinder and quasi-cylinder probabilities and conditional expectation as well as predictio0n and smoothing and the varied processes used in filtering. Especially useful for electronic engineers and mathematical statisticians for explaining the systematic use of finely additive white noise theory leading to a more simplified and direct presentation.
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First Sentence:
The object of this monograph is to develop the theory of Gaussian white noise and apply it to a large class of non-linear filtering, prediction and interpolation problems. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
accessible random variables, cylinder probability, measure determining class, white noise setup, additive probability space, unnormalized conditional density, unnormalized conditional distribution, measure valued equations, cylinder probabilities, countably additive probability measure, density pst, measurable representation, white noise theory, unique classical solution, weak generator, measurable norm, nonlinear filtering theory, countably additive extension, cylindrical mapping, unique generalized solution, finitely additive probability measure, lifting map, filtering model, white noise approach, white noise model Key Phrases - Capitalized Phrases (CAPs): (learn more)
S-valued Markov New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The object of this monograph is to develop the theory of Gaussian white noise and apply it to a large class of non-linear filtering, prediction and interpolation problems. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
accessible random variables, cylinder probability, measure determining class, white noise setup, additive probability space, unnormalized conditional density, unnormalized conditional distribution, measure valued equations, cylinder probabilities, countably additive probability measure, density pst, measurable representation, white noise theory, unique classical solution, weak generator, measurable norm, nonlinear filtering theory, countably additive extension, cylindrical mapping, unique generalized solution, finitely additive probability measure, lifting map, filtering model, white noise approach, white noise model Key Phrases - Capitalized Phrases (CAPs): (learn more)
S-valued Markov New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 16
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11
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- Stochastic Differential Equations (Siam-Ams Proceedings Ser .: No 6) by N. Y.) Symposium in Applied Mathematics (1972 New York in Back Matter (1), and Back Matter (2)
- Great Britain (Discovering Cultures) by Sharon Gordon in Front Matter
- Stochastic Differential Systems, Stochastic Control Theory and Applications: Proceedings of a Workshop, held at IMA, June 9-19, 1986 (The IMA Volumes in Mathematics and its Applications) by Wendell Fleming in Back Matter
- Stochastic Differential Equations 1976: Kyoto: International Symposium Proceedings by Kiyosi Ito in Back Matter
- Stochastic Differential Equations in Infinite Dimensional Spaces (Lecture Notes-Monographs; Vol. 26) by Gopinath Kallianpur in Back Matter
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- Stochastics in Finite and Infinite Dimensions (Trends in Mathematics) by Takeyuki Hida in Front Matter (1), and Front Matter (2)
- Applied Stochastic Analysis (Stochastics Monographs) by M. H.a. Davis in Front Matter
- Malliavin Calcul Procesesses J (Stochastic Monographs : Theory and Applications of Stochastic Processes, Vol 2) by Klaus Bichteler in Front Matter
- Structure Selection of Stochastic Dynamic Systems (Stochastics Monographs) by Sandor M. Veres in Front Matter
- Statistical Methods in Control & Signal Processing (Electrical and Computer Engineering) by Tohru Katayama on page 237
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