| Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering IS Mbalawata, S Särkkä, H Haario Computational Statistics 28, 1195-1223, 2013 | 76 | 2013 |
| Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter IS Mbalawata, S Särkkä, M Vihola, H Haario Computational Statistics & Data Analysis 83, 101-115, 2015 | 53 | 2015 |
| Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC S Särkkä, J Hartikainen, IS Mbalawata, H Haario Statistics and Computing 25 (2), 427-437, 2015 | 32 | 2015 |
| Mathematical modeling of COVID-19 transmission dynamics between healthcare workers and community L Masandawa, SS Mirau, IS Mbalawata Results in Physics 29, 104731, 2021 | 27 | 2021 |
| Analysis of bias in an Ebola epidemic model by extended Kalman filter approach D Ndanguza, IS Mbalawata, H Haario, JM Tchuenche Mathematics and Computers in Simulation 142, 113-129, 2017 | 23 | 2017 |
| Mathematical modeling of vaccination as a control measure of stress to fight COVID-19 infections JN Paul, IS Mbalawata, SS Mirau, L Masandawa Chaos, Solitons & Fractals 166, 112920, 2023 | 15 | 2023 |
| Moment conditions for convergence of particle filters with unbounded importance weights IS Mbalawata, S Särkkä Signal Processing 118, 133-138, 2016 | 13 | 2016 |
| Analysis of SDEs applied to SEIR epidemic models by extended Kalman filter method D Ndanguza, IS Mbalawata, JP Nsabimana Applied Mathematics 7 (17), 2195-2211, 2016 | 12 | 2016 |
| On the L4convergence of particle filters with general importance distributions IS Mbalawata, S Särkkä 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 11 | 2014 |
| Mathematical Approach to Investigate Stress due to Control Measures to Curb COVID‐19 JN Paul, SS Mirau, IS Mbalawata Computational and mathematical methods in medicine 2022 (1), 7772263, 2022 | 10 | 2022 |
| A mathematical model for the dynamics and MCMC analysis of tomato bacterial wilt disease F Remo, LS Luboobi, IS Mabalawata, BK Nannyonga International Journal of Biomathematics 11 (01), 1850001, 2018 | 7 | 2018 |
| Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models IS Mbalawata Lappeenranta University of Technology, 2014 | 7 | 2014 |
| Markov chain Monte Carlo analysis of the variable-volume exothermic model for a continuously stirred tank reactor JP Muhirwa, SI Mbalawata, VG Masanja Engineering, Technology & Applied Science Research 11 (2), 6919-6929, 2021 | 6 | 2021 |
| Continuous time markov chain model for cholera epidemic transmission dynamics YM Marwa, IS Mbalawata, S Mwalili International Journal of Statistics and Probability 8 (3), 1-32, 2019 | 6 | 2019 |
| Markov chain Monte Carlo analysis of cholera epidemic YM Marwa, S Mwalili, IS Mbalawata J. Math. Comput. Sci. 8 (5), 584-610, 2018 | 6 | 2018 |
| Determining parameter distribution in within-host severe P. falciparum malaria B Nannyonga, GG Mwanga, H Haario, IS Mbalawata, M Heilio BioSystems 126, 76-84, 2014 | 6 | 2014 |
| Weight moment conditions for L4convergence of particle filters for unbounded test functions IS Mbalawata, S Särkkä 2014 22nd European Signal Processing Conference (EUSIPCO), 1207-1211, 2014 | 6 | 2014 |
| Modeling nosocomial infection of COVID-19 transmission dynamics L Masandawa, SS Mirau, IS Mbalawata, JN Paul, K Kreppel, OM Msamba Results in physics 37, 105503, 2022 | 5 | 2022 |
| Stochastic dynamics of cholera epidemic model: Formulation, analysis and numerical simulation YM Marwa, IS Mbalawata, S Mwalili, WM Charles Journal of Applied Mathematics and Physics 7 (5), 1097-1125, 2019 | 5 | 2019 |
| Compartmental mathematical modelling of dynamic transmission of COVID-19 in Rwanda L Mpinganzima, JM Ntaganda, W Banzi, JP Muhirwa, BK Nannyonga, ... IJID regions 6, 99-107, 2023 | 4 | 2023 |
