1. Park, B.-J., Zhang, Y., Lord, D. (2010). Bayesian mixture modeling approach to account for heterogeneity in speed data, Transportation Research Part B: Methodological, vol. 44, 5, pp. 662–673.
2. Yoshigoe, K., Dai, W., Abramson, M., Jacobs, A. (2015). Overcoming invasion of privacy in smart home environment with synthetic packet injection, In: TRON Symposium (TRONSHOW), Tokyo, Japan, 2014, pp. 1–7.
3. Yu, J. (2011). Fault detection using principal components-based Gaussian mixture model for semiconductor manufacturing processes, IEEE Transactions on Semiconductor Manufacturing, vol. 24, 3, pp. 432–444.
4. Kárný, M., Böhm, J., Guy, T. V., Jirsa, L., Nagy, I., Nedoma, P., Tesař, L. (2006). Optimized Bayesian dynamic advising: theory and algorithms, Springer-Verlag London.
5. Kárný, M., Kadlec, J., Sutanto, E. L. (1998). Quasi-Bayes estimation applied to normal mixture, in: Preprints of the 3rd European IEEE Workshop on Computer-Intensive Methods in Control and Data Processing (eds. J. Rojíček, M. Valečková, M. Kárný, K. Warwick), CMP’98 /3./, Prague, CZ, pp. 77–82.