A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study

Yet, Barbaros
Fenton, Norman
Neil, Martin
Luedeling, Eike
Shepherd, Keith
Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.


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It is an important issue in the software industry to predict how much effort will be required for a software project as early as possible. Software size is one of the commonly used attributes in effort estimation. In this paper, we propose an early software size and effort estimation method based on conceptual model of the problem domain. Our method utilizes the noteworthy domain concepts identified mainly from the use cases written in the requirements phase of the software development lifecycle. In order t...
Citation Formats
B. Yet, N. Fenton, M. Neil, E. Luedeling, and K. Shepherd, “A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 141–155, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56441.