Fuzzy Bayesian inference for under-five mortality data
M.K. Mwanga,
S.S. Mirau,
J.M. Tchuenche,
I.S. Mbalawata
Affiliations
M.K. Mwanga
School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania; Institute of Accountancy Arusha, P.O. Box 2798, Arusha, Tanzania; Corresponding author at: School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania.
S.S. Mirau
School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania
J.M. Tchuenche
School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology, P.O. Box 447, Arusha, Tanzania
I.S. Mbalawata
African Institute for Mathematical Sciences, NEI Global Secretariat, Rue KG590 ST, Kigali, Rwanda
Under-five mortality remains a significant global health challenge, with millions of children dying before their fifth birthday each year. This study explores the application of fuzzy Bayesian inference for under-five mortality data using Tanzania as a case study. Fuzzy Bayesian inference has emerged as a promising technique that combines the flexibility of fuzzy set theory with the probabilistic framework of Bayesian inference. The study employs fuzzy sets and membership functions to represent the linguistic terms and their degrees of membership, along with the Poisson distribution to model the mortality rate. The results demonstrate the potential of fuzzy Bayesian inference for analyzing under-five mortality rates. This approach provides a more nuanced understanding of the complex mortality patterns.
