Estimating a state-space model from point process observations: A note on convergence

Neural Computation

Volumn 22, Issue 8, 2010, Pages 1993-2001

  Yuan, Ke ^a   Niranjan, Mahesan ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; NERVE CELL; PHYSIOLOGY;

ALGORITHMS; MODELS, NEUROLOGICAL; NEURONS;

EID: 77956008922     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2010.07-09-1047     Document Type: Article

References (8)

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2. Barbieri, R., Matten, E. C., Alabi, A. A., & Brown, E. N. (2005). A point-process model of human heartbeat intervals: New definitions of heart rate and heart rate variability. Am. J. Physiol. Heart Circ. Physiol., 288(1), H424-H435.
3. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. B, 39, 1-38.
4. Eden, U. T., Frank, L. M., Barbieri, R., Solo, V., & Brown, E. N. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation, 16(5), 971-998.
5. Ergun, A., Barbieri, R., Eden, U. T., Wilson, M. A., & Brown, E. N. (2007). Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Transactions on Biomedical Engineering, 54(3), 419-428.
6. Roweis, S., & Ghahramani, Z. (1999). A unifying review of linear gaussian models. Neural Computation, 11, 305-345.
7. Smith, A. C., & Brown, E. N. (2003). Estimating a state-space model from point process observations. Neural Computation, 15(5), 965-991.
8. Wu, C. F. J. (1983). On the convergence properties of the EM algorithm. Ann. Stat., 15, 95-103.