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E.J., A conceptual introduction to Bayesian model averaging. Adv. Methods Pract. Psychol. Sci., 3, 2, 200–215, 2020.

      19. Ji, L., Zhi, X., Zhu, S., Fraedrich, K., Probabilistic precipitation forecasting over East Asia using Bayesian model averaging. Weather Forecasting, 34, 2, 377–392, 2019.

      20. Liu, Z. and Merwade, V., Separation and prioritization of uncertainty sources in a raster based flood inundation model using hierarchical Bayesian model averaging. J. Hydrol., 578, 124100, 2019.

      21. Isupova, O., Li, Y., Kuzin, D., Roberts, S.J., Willis, K., Reece, S., Computer Science, Mathematics, BCCNet: Bayesian classifier combination neural network. arXiv preprint arXiv:1811.12258, 8, 1–5, 2018.

      22. Yang, J., Wang, J., Tay, W.P., Using social network information in community-based Bayesian truth discovery. IEEE Trans. Signal Inf. Process. Networks, 5, 3, 525–537, 2019.

23. Yang, J., Wang, J., Tay, W.P., IEEE Transactions on Signal and Information Processing over Networks, Using Social Network Information in Bayesian Truth Discovery. arXiv preprint arXiv:1806.02954, 5, 525–537, 2018.

      24. Dadhich, S., Sandin, F., Bodin, U., Andersson, U., Martinsson, T., Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders. Autom. Constr., 97, 1–12, 2019.

      25. Leguizamón, S., Jahanbakhsh, E., Alimirzazadeh, S., Maertens, A., Avellan, F., Multiscale simulation of the hydroabrasive erosion of a Pelton bucket: Bridging scales to improve the accuracy. Int. J. Turbomach. Propuls. Power, 4, 2, 9, 2019.

      26. Lora, J.M., Tokano, T., d’Ollone, J.V., Lebonnois, S., Lorenz, R.D., A model intercomparison of Titan’s climate and low-latitude environment. Icarus, 333, 113–126, 2019.

      27. Chen, J., Yin, J., Zang, L., Zhang, T., Zhao, M., Stacking machine learning model for estimating hourly PM2. 5 in China based on Himawari 8 aerosol optical depth data. Sci. Total Environ., 697, 134021, 2019.

      28. Dou, J., Yunus, A.P., Bui, D.T., Merghadi, A., Sahana, M., Zhu, Z., Pham, B.T., Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides, 17, 3, 641–658, 2020.

      29. Singh, S.K., Bejagam, K.K., An, Y., Deshmukh, S.A., Machine-learning based stacked ensemble model for accurate analysis of molecular dynamics simulations. J. Phys. Chem. A, 123, 24, 5190–5198, 2019.

      30. https://archive.ics.uci.edu/ml/index.html

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