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Keywords:
mixture model; Bayesian estimation; approximation; clustering; classification
mixture model; Bayesian estimation; approximation; clustering; classification
Summary:
Probabilistic mixtures provide flexible “universal” approximation of probability density functions. Their wide use is enabled by the availability of a range of efficient estimation algorithms. Among them, quasi-Bayesian estimation plays a prominent role as it runs “naturally” in one-pass mode. This is important in on-line applications and/or extensive databases. It even copes with dynamic nature of components forming the mixture. However, the quasi-Bayesian estimation relies on mixing via constant component weights. Thus, mixtures with dynamic components and dynamic transitions between them are not supported. The present paper fills this gap. For the sake of simplicity and to give a better insight into the task, the paper considers mixtures with known components. A general case with unknown components will be presented soon.
References:
[1] Bernardo, J. M.: Expected information as expected utility. Ann. Statist. 7 (1979), 3, 686–690. DOI 10.1214/aos/1176344689 | MR 0527503
[2] Böhm, J., Kárný, M.: Transformation of user’s knowledge into initial values for identification. In: Preprints DYCOMANS Workshop Industrial Control and Management Methods: Theory and Practice (M. Součková and J. Böhm, eds.), ÚTIA AV ČR, Prague 1995, pp. 17–24.
[3] Chen, W., Jovanis, P.: Method for identifying factors contributing to driver-injury severity in traffic crashes. Highway And Traffic Safety: Crash Data, Analysis Tools, And Statistical Methods 1717 (2000), 1–9.
[4] Chinnaswamy, G., Chirwa, E., Nammi, S., Nowpada, S., Chen, T., Mao, M.: Benchmarking and accident characteristics of flat-fronted commercial vehicles with respect to pedestrian safety. Internat. J. Crashworthiness 12 (2007), 279–291. DOI 10.1080/13588260701441365
[5] Dempster, A. P., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B (Methodological) 39 (1977), 1, 1–38. MR 0501537 | Zbl 0364.62022
[6] Hamilton, J. D., Susmel, R.: Autoregressive conditional heteroskedasticity and changes in regime. J. Econometrics 64 (1994), 307–333. DOI 10.1016/0304-4076(94)90067-1 | Zbl 0825.62950
[7] Haykin, S.: Neural Networks: A Comprehensive Foundation. MacMillan, New York 1994. Zbl 0828.68103
[8] Wang, Jianyong, Zhang, Yuzhou, Zhou, Lizhu, Karypis, G. , Aggarwal, Charu C.: Contour: An efficient algorithm for discovering discriminating subsequences. In: Data Mining and Knowledge Discovery, Springer 18 (2009), 1, pp. 1–29. MR 2469590
[9] Kárný, M.: Tools for computer-aided design of adaptive controllers. IEE Control Theory Appl. 150 (2003), 6, 643.
[10] Kárný, M., Böhm, J., Guy, T. V., Jirsa, L., Nagy, I., Nedoma, P., Tesař, L.: Optimized Bayesian Dynamic Advising: Theory and Algorithms. Springer, London 2005.
[11] Kárný, M., Kadlec, J., Sutanto, E. L.: Quasi-Bayes estimation applied to normal mixture. in In: Preprints 3rd European IEEE Workshop on Computer-Intensive Methods in Control and Data Processing (J. Rojíček, M. Valečková, M. Kárný, and K. Warwick, eds.), ÚTIA AV ČR, Prague 1998, pp. 77–82.
[12] Kárný, M., Nagy, I., Novovičová, J.: Mixed-data multi-modelling for fault detection and isolation. Internat. J. Adaptive Control Signal Process. 16 (2002), 1, 61–83. DOI 10.1002/acs.672 | Zbl 0998.93016
[13] Kerridge, D. F.: Inaccuracy and Inference. J. Royal Statist. Soc. Ser. B (Methodological) 23 (1961), 1, 184–194. MR 0123375 | Zbl 0112.10302
[14] Kulhavý, R.: A Bayes-closed approximation of recursive non-linear estimation. Internat. J. Adaptive Control Signal Process. 4 (1990), 271–285. DOI 10.1002/acs.4480040404
[15] Ljung, L.: System Identification: Theory for the User. Prentice-Hall, London 1987. Zbl 0615.93004
[16] Murray-Smith, R., Johansen, T.: Multiple Model Approaches to Modelling and Control. Taylor &Francis, London 1997. MR 1694153
[17] Oppenheim, A., Wilsky, A.: Signals and Systems. Englewood Clifts, Jersey 1983.
[18] Opper, M., Saad, D.: Advanced Mean Field Methods: Theory and Practice. The MIT Press, Cambridge 2001. MR 1863214 | Zbl 0994.68172
[19] Qu, H. B., Hu, B. G.: Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth. Ecological Informatics 4 (2009), 3, 163–176. DOI 10.1016/j.ecoinf.2009.06.004
[20] Roberts, W. A.: Convex Functions. Academic Press, New York 1973. MR 0442824 | Zbl 0271.26009
[21] Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: Density-based clustering in spatial databases: The algorithm gdbscan and its applications. In: Data Mining and Knowledge Discovery, Springer, 2 (1998), 2, pp. 169–194.
[22] Titterington, D., Smith, A., Makov, U.: Statistical Analysis of Finite Mixtures. John Wiley, New York 1985. MR 0838090
[23] Xu, Xiaowei, Jäger, J., Kriegel, H.-P.: A fast parallel clustering algorithm for large spatial databases. In: Data Mining and Knowledge Discovery, Springer, 3 (1999), 3, pp. 263–290.
[24] Zhang, T., Ramakrishnan, R., Livny, M.: Birch: A new data clustering algorithm and its applications. In: Data Mining and Knowledge Discovery, Springer, 1 (1997), 2, pp. 141–182.
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