A Practical Study of Biological Big Data Base Assisting the Reformed Teaching of Probability Theory and Mathematical Statistics Course

[1] Meyer, M. C. (2019). Probability and Mathematical Statistics: Theory, Applications, and Practice in R. Society for Industrial and Applied Mathematics. Search in Google Scholar

[2] Gnedenko, B. V. (2018). Theory of probability. Routledge. Search in Google Scholar

[3] Durrett, R. (2019). Probability: theory and examples (Vol. 49). Cambridge university press. Search in Google Scholar

[4] Watanabe, S. (2018). Mathematical theory of Bayesian statistics. chapman and hall/cRc. Search in Google Scholar

[5] Rossi, R. J. (2018). Mathematical statistics: an introduction to likelihood based inference. John Wiley & Sons. Search in Google Scholar

[6] van der Vaart, A., Jonker, M., & Bijma, F. (2017). An introduction to mathematical statistics. Amsterdam University Press. Search in Google Scholar

[7] Rasch, D., & Schott, D. (2018). Mathematical statistics. John Wiley & Sons. Search in Google Scholar

[8] Spanos, A. (2019). Probability theory and statistical inference: Empirical modeling with observational data. Cambridge University Press. Search in Google Scholar

[9] Kapadia, A. S., Chan, W., & Moyé, L. A. (2017). Mathematical statistics with applications. CRC Press. Search in Google Scholar

[10] Gupta, S. C., & Kapoor, V. K. (2020). Fundamentals of mathematical statistics. Sultan Chand & Sons. Search in Google Scholar

[11] Du, J., & Hu, H. (2024, May). Research on Teaching Reform Strategy of Engineering Mathematics Based on Information Technology. In 2024 5th International Conference on Big Data and Informatization Education (ICBDIE 2024) (pp. 428-437). Atlantis Press. Search in Google Scholar

[12] Hoffman-Jorgensen, J. (2017). Probability with a View towards Statistics, volume I. Routledge. Search in Google Scholar

[13] Liu, Z. (2017). Teaching reform of business statistics in college and university. EURASIA Journal of Mathematics, Science and Technology Education, 13(10), 6901-6907. Search in Google Scholar

[14] Olpak, Y. Z., Baltaci, S., & Arican, M. (2018). Investigating the effects of peer instruction on preservice mathematics teachers’ achievements in statistics and probability. Education and Information Technologies, 23, 2323-2340. Search in Google Scholar

[15] Kandeel, R. A. A. (2019). Students’ Academic Difficulties in Learning a Statistics and Probability Course: The Instructors’ View. Journal of Education and Practice, 10(9), 43-52. Search in Google Scholar

[16] Elbehary, S. G. A. (2020). Teacher education of statistics from theory to practice. Journal of Applied Research in Higher Education, 12(5), 857-869. Search in Google Scholar

[17] Li, Y. (2017, December). Research on Classroom Teaching Model Innovation of Statistics Course under the Background of Blended Learning. In 4th International Conference on Education, Management, Arts, Economics and Social Science (ICEMAESS 2017) (pp. 541-545). Atlantis Press. Search in Google Scholar

[18] Yusupova, A. K., & Aliyev, N. A. (2021). SOME CONSIDERATIONS FOR TEACHING PROBABILITY THEORY AND MATHEMATICAL STATISTICS. Oriental renaissance: Innovative, educational, natural and social sciences, 1(11), 1183-1191. Search in Google Scholar

[19] Wang, F., & Xu, X. P. (2018, April). Research on the Teaching of Probability Theory and Mathematical Statistics for Non-statistics Majors. In 2018 International Conference on Education Reform and Management Science (ERMS 2018) (pp. 208-212). Atlantis Press. Search in Google Scholar

[20] Jiang, Y. (2022, November). Research on the Teaching Reform of Integrating Mathem-atical Modeling into Probability Theory and Mathematical Statistics. In 2022 3rd International Conference on Information Science and Education (ICISE-IE) (pp. 119-122). IEEE. Search in Google Scholar

[21] Wen, X. (2022). Exploration and Practice of Ideological and Political Teaching Reform in Probability Theory and Mathematical Statistics Course [J]. Frontiers in Educational Research, 5(20). Search in Google Scholar

[22] Zeng, L. (2020, November). Application of MATLAB in the teaching of probability theory and mathematical statistics. In Journal of Physics: Conference Series (Vol. 1651, No. 1, p. 012078). IOP Publishing. Search in Google Scholar

[23] Tian, X. (2021). Research on Teaching Reform of Probability Theory and Mathematical Statistics in Artificial Intelligence Major. Frontiers in Educational Research, 4(11), 110-114. Search in Google Scholar

[24] Wang, J. (2024). Teaching Reform of “Probability Theory and Mathematical Statistics” Under the Background of New Engineering. Journal of Contemporary Educational Research, 8(3), 32-37. Search in Google Scholar

[25] Hang, L., Junyang, P., Dunhui, G., & Yao, C. (2023). Research and Practice on the Teaching Reform of Probability Theory and Mathematical Statistics Based on Knowledge Visualization. Transactions on Comparative Education, 5(7), 59-67. Search in Google Scholar

[26] Muhammad Amir Khan,Bilal Asad,Toomas Vaimann & Ants Kallaste. (2024). An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology. Machines(7),495-495. Search in Google Scholar

[27] Jianguo Miao,Xuanxuan Liu,Li Guo & Long Chen. (2024). SE-BLS: A Shapley-Value-Based Ensemble Broad Learning System with collaboration-based feature selection and CAM visualization. Knowledge-Based Systems112343-112343. Search in Google Scholar

[28] Leng Hongyong,Chen Cheng,Chen Chen,Chen Fangfang,Du Zijun,Chen Jiajia... & Liu Pei. (2023). Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 121839-121839. Search in Google Scholar

[29] Muhammad Kamran Khan,Mohamad Abou Houran,Kimmo Kauhaniemi,Muhammad Hamza Zafar,Majad Mansoor & Saad Rashid. (2024). Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA–CNN–Bi-LSTM deep learning model. Heliyon(15),e35183-e35183. Search in Google Scholar