Sequential Bayesian prediction in the presence of changepoints

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 345-352

  Garnett, Roman ^a   Osborne, Michael A ^a   Roberts, Stephen J ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; CHANGE-POINTS; CO-VARIANCE; COVARIANCE FUNCTION; GAUSSIAN PROCESSES; HYPERPARAMETERS; NONSTATIONARY; POSTERIOR DISTRIBUTIONS; PREDICTION ALGORITHMS; PREDICTIVE DISTRIBUTIONS; SEQUENTIAL ALGORITHM;

ALGORITHMS; BAYESIAN NETWORKS; FORECASTING; LOCATION; ROBOT LEARNING;

SEQUENTIAL SWITCHING;

EID: 71149109292     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Adams, R. P., & MacKay, D. J. (2007). Bayesian online changepoint detection (Technical Report). University of Cambridge, Cambridge, UK. arXiv:0710.3742vl [stat.ML].
2. Basseville, M., & Nikiforov, I. (1993). Detection of abrupt changes: theory and application. Prentice Hall.
3. Brodsky, B., & Darkhovsky, B. (1993). Nonparametric Methods in Change-Point Problems. Springer.
4. Carlin, B. P., Gelfand, A. E., & Smith, A. F. M. (1992). Hierarchical Bayesian analysis of change-point problems. Applied statistics, 41, 389-405.
5. Chen, J., & Gupta, A. (2000). Parametric Statistical Change Point Analysis. Birkháuser Verlag.
6. Chernoff, H., & Zacks, S. (1964). Estimating the Current Mean of a Normally Distributed Variable Which is Subject to Changes in Time. Annals of Mathematical Statistics, 35, 999-1028.
7. Csorgo, M., & Horvath, L. (1997). Limit theorems in change-point analysis. John Wiley & Sons.
8. Fearnhead, P., & Liu, Z. (2007). On-line inference for multiple changepoint problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 589-605.
9. Horváth, L., & Kokoszka, P. (1997). The effect of long-range dependence on change-point estimators. Journal of Statistical Planning and Inference, 64, 57-81.
10. Muller, H. (1992). Change-points in nonparametric regression analysis. Ann. Statist, 20, 737-761.
11. Osborne, M. A., Rogers, A., Ramchurn, S., Roberts, S. J., & Jennings, N. R. (2008). Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes. International Conference on Information Processing in Sensor Networks 2008 (pp. 109-120).
12. Rasmussen, C.E., & Ghahramani, Z. (2003). Bayesian Monte Carlo. In S. Becker and K. Obermayer (Eds.), Advances in neural information processing systems, vol. 15. Cambridge, MA: MIT Press.
13. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
14. Ray, B., & Tsay, R. (2002). Bayesian methods for change-point detection in long-range dependent processes. Journal of Time Series Analysis, 23, 687-705.
15. Roberts, S. J. (2000). Extreme value statistics for novelty detection in biomedical data processing. Science, Measurement and Technology, IEE Proceedings. (pp. 363-367).
16. Whitcher, B., Byers, S., Guttorp, P., & Percival, D. (2002). Testing for homogeneity of variance in time series: Long memory, wavelets and the Nile River. Water Resources Research, 38, 10-1029.