Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-29T04:10:45.075Z Has data issue: false hasContentIssue false

1 - Inference and estimation in probabilistic time series models

Published online by Cambridge University Press:  07 September 2011

David Barber
Affiliation:
University College London
A. Taylan Cemgil
Affiliation:
Engineering Boğaziçi University
Silvia Chiappa
Affiliation:
University of Cambridge
David Barber
Affiliation:
University College London
A. Taylan Cemgil
Affiliation:
Boğaziçi Üniversitesi, Istanbul
Silvia Chiappa
Affiliation:
University of Cambridge
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] B. D., Anderson and J. B., Moore. Optimal Filtering. Prentice-Hall, 1979.Google Scholar
[2] G., Box, G. M., Jenkins and G., Reinsel. Time Series Analysis: Forecasting and Control. Prentice Hall, 1994.Google Scholar
[3] O., Cappé, R., Douc and E., Moulines. Comparison of resampling schemes for particle filtering. In 4th International Symposium on Image and Signal Processing and Analysis, pages 64–69, 2005.Google Scholar
[4] O., Cappé, E., Moulines and T., Rydén. Inference in Hidden Markov Models. Springer-Verlag, 2005.Google Scholar
[5] A., Dempster, N., Laird and D., Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1–38, 1977.Google Scholar
[6] A., Doucet, N., de Freitas and N. J., Gordon, editors. Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.Google Scholar
[7] A., Doucet, S., Godsill and C., Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3):197–208, 2000.Google Scholar
[8] A., Doucet and A. M., Johansen. A tutorial on particle filtering and smoothing: fifteen years later. Handbook of Nonlinear Filtering. Oxford University Press, 2010.Google Scholar
[9] J., Durbin. The fitting of time series models. Rev. Inst. Int. Stat., 28, pages 233–243, 1960.Google Scholar
[10] S., Geman and D., Geman. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. In M. A., Fischler and O., Firschein, editors, Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, pages 564–584. Kaufmann, 1987.Google Scholar
[11] F., Gustafsson. Adaptive filtering and change detection. John Wiley & Sons, 2000.Google Scholar
[12] W. K., Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57:97–109, 1970.Google Scholar
[13] A. M., Johansen, L., Evers and N., Whiteley. Monte Carlo Methods, Lecture Notes, Department of Mathematics, Bristol University, 2008.Google Scholar
[14] R. E., Kalman. A new approach to linear filtering and prediction problems. Transaction of the ASME-Journal of Basic Engineering, 35–45, 1960.Google Scholar
[15] S., Kotz, N., Balakrishnan and N. L., Johnson. Continuous Multivariate Distributions, volume 1, Models and Applications. John Wiley & Sons, 2000.Google Scholar
[16] S. L., Lauritzen. Thiele: Pioneer in Statistics. Oxford University Press, 2002.Google Scholar
[17] J. S., Liu. Monte Carlo Strategies in Scientific Computing. Springer, 2004.Google Scholar
[18] N., Metropolis and S., Ulam. The Monte Carlo method. Journal of the American Statistical Association, 44(247):335–341, 1949.Google Scholar
[19] T., Minka. Expectation Propagation for approximate Bayesian inference. PhD thesis, MIT, 2001.
[20] M., Mitzenmacher and E., Upfal. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press, 2005.Google Scholar
[21] J. R., Norris. Markov Chains. Cambridge University Press, 1997.Google Scholar
[22] P., Park and T., Kailath. New square-root smoothing algorithms. IEEE Transactions on Automatic Control, 41:727–732, 1996.Google Scholar
[23] L. R., Rabiner. A tutorial on hidden Markov models and selected applications in speech recognation. Proceedings of the IEEE, 77(2):257–286, 1989.Google Scholar
[24] H., Rauch, F., Tung and C., Striebel. Maximum likelihood estimates of linear dynamic systems. American Institute of Aeronautics and Astronautics Journal, 3(8):1445–1450, 1965.Google Scholar
[25] R. L., Stratonovich. Application of the Markov processes theory to optimal filtering. Radio Engineering and Electronic Physics, 5(11):1–19, 1960. Translated from Russian.Google Scholar
[26] M., Verhaegen and P., Van Dooren. Numerical Aspects of Different Implementations. IEEE Transactions On Automatic Control, 31(10):907–917, 1986.Google Scholar
[27] M., Wainwright and M. I., Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1:1–305, 2008.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×