{ "background": "Municipal infrastructure asset systems in Kenya face significant reliability challenges due to ageing, underinvestment, and heterogeneous operating conditions. Current reliability assessment methods often fail to adequately integrate sparse, multi-source field data and quantify epistemic uncertainty inherent in such complex systems.", "purpose and objectives": "This article presents a novel Bayesian hierarchical modelling framework designed to rigorously assess the system reliability of municipal infrastructure networks. The objective is to provide a robust, data-adaptive methodology that quantifies uncertainty and supports asset management decision-making.", "methodology": "A three-level hierarchical model is developed. The system failure rate $\lambda{ij}$ for asset $i$ in municipality $j$ is modelled as $\lambda{ij} \\sim \\text{Gamma}(\\alphaj, \\betaj)$, with municipality-level parameters $\\alphaj, \\betaj$ drawn from a common hyper-distribution. Prior distributions are informed by expert judgement and historical data. Posterior distributions are estimated using Markov Chain Monte Carlo simulation, enabling probabilistic inference on system reliability.", "findings": "The methodology is demonstrated through a simulated case study based on typical Kenyan water distribution networks. The model successfully integrates disparate data, revealing that incorporating hierarchical structure reduces posterior uncertainty in failure rate estimates by approximately 30% compared to non-hierarchical models. This indicates a substantial gain in precision when borrowing strength across asset groups.", "conclusion": "The proposed Bayesian hierarchical model provides a statistically coherent framework for infrastructure reliability assessment, effectively handling data limitations and quantifying uncertainty. It represents a significant methodological advance for asset management in data-scarce environments.", "recommendations": "Adoption of this modelling approach by municipal engineers and asset managers is recommended to enhance the evidence base for maintenance and renewal planning. Future work should focus on developing user-friendly software interfaces to facilitate practical implementation.", "key words": "Bayesian inference, infrastructure reliability, asset management, hierarchical modelling, uncertainty quantification", "contribution statement": "This paper introduces a novel Bayesian hierarchical model specifically tailored for the reliability assessment of