@inproceedings{0af4dd964c2346e9866ab81fb513aa78,
title = "Learning in Gaussian Markov random fields",
abstract = "This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information on the states and improve the quality of the state estimates, it is necessary to learn this unknown parameter first. Here we assume a parameterized Gaussian Markov random field to model the prior distribution of the states and propose an algorithm that is able to learn its parameters from given observations on these states. The effectiveness of this approach is proven experimentally by simulations.",
keywords = "EM algorithm, Factor graph, Gaussian Markov random field, Sum-product algorithm, Unsupervised learning",
author = "Riedl, \{Thomas J.\} and Singer, \{Andrew C.\} and Choi, \{Jun Won\}",
year = "2010",
doi = "10.1109/ICASSP.2010.5496112",
language = "English",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3070--3073",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
note = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
}