Sequential bayesian prediction in the presence of changepoints and faults

Computer Journal

Volumn 53, Issue 9, 2010, Pages 1430-1446

  Garnett, Roman ^a   Osborne, Michael A ^a   Reece, Steven ^a   Rogers, Alex ^b   Roberts, Stephen J ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Bayesian methods; changepoint detection; fault detection; Gaussian processes; time series prediction

Indexed keywords

BAYESIAN; BAYESIAN METHODS; BAYESIAN PREDICTIONS; CHANGE POINT DETECTION; CHANGE-POINTS; COVARIANCE FUNCTION; GAUSSIAN PROCESSES; HYPER-PARAMETERS; NON-STATIONARY COVARIANCE FUNCTIONS; OBSERVATION MODEL; POSTERIOR DISTRIBUTIONS; PREDICTION ALGORITHMS; PREDICTIVE DISTRIBUTIONS; SENSOR FAULT; SEQUENTIAL ALGORITHM; TIME SERIES PREDICTION;

ALGORITHMS; BAYESIAN NETWORKS; FAULT DETECTION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LOCATION; SENSORS;

FORECASTING;

EID: 78149279501     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxq003     Document Type: Article

References (30)

1. Basseville, M. and Nikiforov, I. (1993) Detection of Abrupt Changes: Theory and Application. Prentice Hall.
2. Brodsky, B. and Darkhovsky, B. (1993) Nonparametric Methods in Change-Point Problems. Springer.
3. Csorgo, M. and Horvath, L. (1997) Limit Theorems in Change-Point Analysis. John Wiley & Sons.
4. Chen, J. and Gupta, A. (2000) Parametric Statistical Change Point Analysis. Birkhäuser Verlag.
5. Chernoff, H. and Zacks, S. (1964) Estimating the current mean of a normally distributed variable which is subject to changes in time. Ann. Math. Stat., 35, 999-1028.
6. Adams, R. P. and MacKay, D. J. (2007) Bayesian Online Changepoint Detection. Technical Report, University of Cambridge, Cambridge, UK. arXiv:0710.3742v1[stat. ML].
7. Carlin, B. P., Gelfand, A. E. and Smith, A. F. M. (1992) Hierarchical Bayesian analysis of changepoint problems. Appl. Stat., 41, 389-405.
8. Ray, B. and Tsay, R. (2002) Bayesian methods for change-point detection in long-range dependent processes. J. Time Ser. Anal., 23, 687-705.
9. Muller, H. (1992) Change-points in nonparametric regression analysis. Ann. Stat., 20, 737-761.
10. Horváth, L. and Kokoszka, P. (1997) The effect of long-range dependence on change-point estimators. J. Stat. Plan. Inference, 64, 57-81. (Pubitemid 127161582)
11. Fearnhead, P. and Liu, Z. (2007) On-line inference for multiple changepoint problems. J. R. Stat. Soc.: Ser. B (Stat. Methodol.), 69, 589-605.
12. Venkatasubramanian, V., Rengaswamy, R., Yin, K. and Kavuri, S. (2003) A review of process fault detection and diagonsis. part 1: Quantitative model-based methods. Comput. Chem. Eng., 27, 293-311. (Pubitemid 36285962)
13. Willsky, A. (1976) A survey of design methods for failure detection in dynamic systems. Automatica, 12, 601-611.
14. Basseville, M. (1988) Detecting changes in signals and systems - a survey. Automatica, 24, 309-326. (Pubitemid 18645915)
15. Kobayashi, T. and Simon, D. (2003) Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnosis. Proc. ASME Turbo Expo 2003, Power for Land, Sea, and Air, Atlanta, Georgia, USA, June 16-19, 2003.
16. Aggarwal, V., Nagarajan, K. and Slatton, K. (2004) Estimating Failure Modes Using a Multiple-Model Kalman Filter. Technical Report no. Rep-2004-03-001, ASPL.
17. Reece, S., Claxton, C., Nicholson, D. and Roberts, S. J. (2009a) Multi-sensor Fault Recovery in the Presence of Known and Unknown Fault Types. Proc. 12th Int. Conf. Information Fusion (FUSION 2009), Seattle, USA.
18. Garnett, R., Osborne, M. A. and Roberts, S. (2009) Sequential Bayesian Prediction in the Presence of Changepoints. Proc. 26th Annual Int. Conf. Machine Learning, Montreal, Canada.
19. Reece, S., Garnett, R., Osborne, M. A. and Roberts, S. J. (2009b) Anomaly Detection and Removal Using Non-stationary Gaussian Processes. Technical Report, University of Oxford, Oxford, UK.
20. Rasmussen, C. E. and Williams, C. K. I. (2006) Gaussian Processes for Machine Learning. MIT Press.
21. Osborne, M. A., Rogers, A., Ramchurn, S., Roberts, S. J. and Jennings, N. R. (2008) Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes. Int. Conf. Information Processing in Sensor Networks 2008, St. Louis, MO, USA, pp. 109-120.
22. O'Hagan, A. (1991) Bayes-hermite quadrature. J. Stat. Plan. Inference, 29, 245-260.
23. Whitcher, B., Byers, S., Guttorp, P. and Percival, D. (2002) Testing for homogeneity of variance in time series: Long memory, wavelets and the Nile River. Water Resour. Res., 38, 10-1029. (Pubitemid 34915495)
24. Ruanaidh, J., Fitzgerald, W. and Pope, K. (1994) Recursive Bayesian Location of a Discontinuity in Time Series. Proc. Acoustics, Speech, and Signal Processing, 1994 on IEEE Int. Conf., Vol. 04, pp. 513-516.
25. Ji, S., Krishnapuram, B. and Carin, L. (2006) Variational Bayes for continuous hidden Markov models and its applicationto active learning. IEEE Trans. Pattern Anal. Mach. Intell., 28, 522-532. (Pubitemid 43264651)
26. Lee, H. (2009) Variational Bayesian Hidden Markov Models with Mixtures of Gaussian Emission. Technical Report, University of Oxford, Oxford, UK. http://www.robots.ox.ac.uk/~mosb/Lee2009.pdf.
27. Roberts, S. J. (2000) Extreme Value Statistics for Novelty Detection in Biomedical Data Processing. Science, IEE Proc. Sci. Meas. Technol., pp. 363-367.
28. Faul, S., Gregorcic, G., Boylan, G., Marnane, W., Lightbody, G. and Connolly, S. (2007) Gaussian process modeling of EEG for the detection of neonatal seizures. IEEE Trans. Biomed. Eng., 54, 2151-2162. (Pubitemid 350157793)
29. Roberts, S. J., Everson, R., Rezek, I., Anderer, P. and Schlögl, A. (1999) Tracking ICA for Eye-Movement Artefact Removal. Proc. EMBEC'99, Vienna.
30. Jürgens, R., Becker, W. and Kornhuber, H. (1981) Natural and drug-induced variations of velocity and duration of human saccadic eye movements: evidence for a control of the neural pulse generator by local feedback. Biol. Cyber., 39, 87-96. (Pubitemid 11187075)