AR model parameter estimation: From factor graphs to algorithms

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 5, Issue , 2004, Pages

  Korl, Sascha ^a   Loeliger, Hans Andrea ^a   Lindgren, Allen G ^b  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF RHODE ISLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTO-REGRESSIVE (AR) MODELS; FACTOR GRAPHS; MESSAGE PASSING ALGORITHM; NOISE VARIANCE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; ENCODING (SYMBOLS); GAUSSIAN NOISE (ELECTRONIC); GRAPH THEORY; KALMAN FILTERING; MATHEMATICAL MODELS; MATRIX ALGEBRA; MONTE CARLO METHODS; SIGNAL PROCESSING; VECTORS; WHITE NOISE;

PARAMETER ESTIMATION;

EID: 4544255307     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger, "Factor graphs and the sum-product-algorithm," IEEE Trans. on Information Theory, vol. 47, no. 2, pp. 498-519, 2001.
2. H.-A. Loeliger, "An introduction to factor graphs," IEEE Signal Processing Mag., Jan. 2004.
3. H.-A. Loeliger, "Least squares and Kalman filtering on Forney graphs," in Codes, Graphs, and Systems, R.E. Blahut and R. Koetter, Eds. Kluwer, 2002.
4. T. P. Minka, A Family of Algorithms for Approximate Bayesian Inference, Ph.D. thesis, MIT, 2001.
5. A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, Springer-Verlag, 2001.