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Stochastic Systems: Modeling, Identification and Optimization, I
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Extension of Clark’s innovations equivalence theorem to the case of signal z . independent of noise, with ∫0t z s2ds<∞ a.s.
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Chapter 2
Parametrically stochastic linear differential equations
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Chapter 3
Entrance-exit distributions for Markov additive processes
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Chapter 4
Martingales of a jump process and absolutely continuous changes of measure
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Chapter 5
Analysis of Brownian functionals
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Chapter 6
Probabilistic representations of boundary layer expansions
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Chapter 7
Limit theorems and diffusion approximations for density dependent Markov chains
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Chapter 8
The choice of a stochastic model for a noise system
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Chapter 9
Asymptotic stability and angular convergence of stochastic systems
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Chapter 10
Remarks on wide sense equivalents of continuous Gauss-Markov processes in Rn
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Chapter 11
A reduced dimensionality method for the steady-state Kalman filter
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Chapter 12
On continuous and discrete sampling for parameter estimation in diffusion type processes
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Chapter 13
On integrals in multi-output discrete-time Kalman-Bucy filtering
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Chapter 14
On consistency and identifiability
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Chapter 15
Identification and estimation of the error-in-variables model (EVM) in structural form
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Chapter 16
Value of information in zero-sum games
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Chapter 17
Sequential decision and stochastic control
Overall attention for this book and its chapters
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Mentioned by
syllabi
1
institution with syllabi
wikipedia
2
Wikipedia pages
Citations
dimensions_citation
6
Dimensions
Readers on
mendeley
13
Mendeley
Book overview
1. Extension of Clark’s innovations equivalence theorem to the case of signal z . independent of noise, with ∫0t z s2ds<∞ a.s.
2. Parametrically stochastic linear differential equations
3. Entrance-exit distributions for Markov additive processes
4. Martingales of a jump process and absolutely continuous changes of measure
5. Analysis of Brownian functionals
6. Probabilistic representations of boundary layer expansions
7. Limit theorems and diffusion approximations for density dependent Markov chains
8. The choice of a stochastic model for a noise system
9. Asymptotic stability and angular convergence of stochastic systems
10. Remarks on wide sense equivalents of continuous Gauss-Markov processes in Rn
11. A reduced dimensionality method for the steady-state Kalman filter
12. On continuous and discrete sampling for parameter estimation in diffusion type processes
13. On integrals in multi-output discrete-time Kalman-Bucy filtering
14. On consistency and identifiability
15. Identification and estimation of the error-in-variables model (EVM) in structural form
16. Value of information in zero-sum games
17. Sequential decision and stochastic control
Summary
Syllabi
Wikipedia
Dimensions citations
This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
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syllabus from an institution on Open Syllabus Project.
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English