Staff Profiles

2016: Master of Arts in Statistics, University of Botswana

2008: Bachelor of Science Degree in Statistics, University of Botswana

2006: Diploma in Statistics, University of Botswana

Boikanyo Mokgweetsi is currently a lecturer at the University of Botswana in the Department of Statistics. He is educated at the University of Botswana with Diploma in Statistics, BSc Degree in Statistics, and MA in Statistics. He has been at the University of Botswana since 2013 where he rose through the ranks from a Staff Development Fellow to Lecturer position.

His research Interest Include Bayesian inference and Applications, machine learning/Artificial Neural Networks, and Spatial Statistics. Mr. Boikanyo Mokgweetsi is involved with many professional service activities within and outside the University of Botswana.

•Statistical Distributions

•Statistical Modelling and Applications

•Probability

•Mathematics for Business and Social Sciences

•Statistical Computing

Computer Literacy and Familiarity
Microsoft Office (Word, Excel, PowerPoint) • R Software • SPSS • LaTex Software • WinBUGS Software • BayesX Software • Stan Software • Python

• Bayesian inference

• Development and Application of Bayesian inference Methods in HIV/TB research

• Actuarial science methods and applications to non-life insurance

• Bayesian machine learning/Artificial Neural Networks in Disease Mapping

• Spatial Statistics

• Statistics for Real Estate Valuation

• Ntseane, D. M., Ali, J., Hallez, K., Mokgweetsi, B., Kasule, M., & Kass, N. E. (2020). The features and qualities of online training modules in research ethics: a case study evaluating their institutional application for the University of Botswana. Global Bioethics, 31(1), 133-154.

• Thupeng, W. M., Mokgweetsi, B., & Mothupi, T. (2019). Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior. American Journal of Theoretical and Applied Statistics, 8(6), 287-295.

•Thupeng, W. M., Mothupi, T., Mokgweetsi, B., Mashabe, B., & Sediadie, T. (2018). A Principal Component Regression Model, for Forecasting Daily Peak Ambient Ground Level Ozone Concentrations, in the Presence of Multicollinearity Amongst Precursor Air Pollutants and Local Meteorological Conditions: A Case Study of Maun. International Journal of Applied Mathematics & Statistical Sciences ( IJAMSS ), 7(1). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3128175