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Keywords
(13)
Difference Equation
Indexation
Markov Process
Noise Estimation
Random Field
Random Process
Random Variable
Recursive Estimation
White Noise
Gauss Markov Random Field
kalman filter
Markov Random Field
rauch tung striebel
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Telescoping Recursive Representations and Estimation of GaussMarkov Random Fields
Telescoping Recursive Representations and Estimation of GaussMarkov Random Fields,10.1109/TIT.2011.2104612,IEEE Transactions on Information Theory,Di
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Telescoping Recursive Representations and Estimation of GaussMarkov Random Fields
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Citations: 2
)
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Divyanshu Vats
,
José M. F. Moura
We present \emph{telescoping} recursive representations for both continuous and discrete indexed noncausal GaussMarkov random fields. Our recursions start at the boundary (a hypersurface in $\R^d$, $d \ge 1$) and telescope inwards. For example, for images, the telescoping representation reduce recursions from $d = 2$ to $d = 1$, i.e., to recursions on a single dimension. Under appropriate conditions, the recursions for the
random field
are linear stochastic differential/difference equations driven by white noise, for which we derive
recursive estimation
algorithms, that extend standard algorithms, like the KalmanBucy filter and the RauchTungStriebel smoother, to noncausal Markov random fields.
Journal:
IEEE Transactions on Information Theory  TIT
, vol. 57, no. 3, pp. 16451663, 2011
DOI:
10.1109/TIT.2011.2104612
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Citation Context
(1)
...Recently, we derived a recursive representation for GaussMarkov random fields where the recursions initiate at the boundary and telescope inwards [
6
]...
...Recursive representations and recursive estimators for Gaussian reciprocal processes have been derived in [
6
], [12], [13]...
...we can directly use the results of [
6
] to derive the recursive representation...
...Proof: The proof follows similar steps as the proof of Theorem 9 in [
6
]...
Divyanshu Vats
,
et al.
Reciprocal fields: A model for random fields pinned to two boundaries
References
(36)
Random fields: Analysis and synthesis
(
Citations: 445
)
E. Vanmarcke
Published in 1983.
Gaussian Markov Random Fields: Theory and Applications
(
Citations: 138
)
H. Rue
,
L. Held
Published in 2005.
Modelling and Simulation of Images by Reciprocal Processes
(
Citations: 4
)
Giorgio Picci
,
Francesca Carli
Conference:
International Conference on Computer Modeling and Simulation  UKSIM
, 2008
Classification of binary random patterns
(
Citations: 114
)
K. Abend
,
T. Harley
,
L. Kanal
Journal:
IEEE Transactions on Information Theory  TIT
, vol. 11, no. 4, pp. 538544, 1965
Twodimensional Bayesian estimate of images
(
Citations: 78
)
A. Habibi
Journal:
Proceedings of The IEEE  PIEEE
, vol. 60, no. 7, pp. 878883, 1972
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Citations
(2)
Graphical Models as BlockTree Graphs
(
Citations: 2
)
Divyanshu Vats
,
José M. F. Moura
Journal:
Computing Research Repository  CORR
, vol. abs/1007.0, 2010
Reciprocal fields: A model for random fields pinned to two boundaries
(
Citations: 1
)
Divyanshu Vats
,
José M. F. Moura
Conference:
Conference on Decision and Control  CDC
, pp. 942947, 2010