Background and Objectives: Measurement of arterial compliance is recognized as important for clinical use and for enabling better understanding of circulatory system regulation mechanisms. Estimation of arterial compliance involves either a direct measure of the ratio between arterial volume and pressure changes or an inference from the pulse wave velocity (PWV). In this study we demonstrate an approach to assess arterial compliance by fusion of these two information sources. The approach is based on combining oscillometry as used for blood pressure inference and PWV measurements based on ECG/PPG. Enabling reliable arterial compliance measurements will contribute to the understanding of regulation mechanisms of the arterial tree, possibly establishing arterial compliance as a key measure relevant in hemodynamic monitoring. Methods: A measurement strategy, a physiological model, and a framework based on Bayesian principles are developed for measuring changes in arterial compliance based on combining oscillometry and PWV data. A simulation framework is used to study and validate the algorithm and measurement principle in detail, motivated by previous experimental findings. Results: Simulations demonstrate the possibility of inferring arterial compliance via fusion of simultaneously acquired volume/pressure relationships and PWV data. In addition, the simulation framework demonstrates how Bayesian principles can be used to handle low signal – to – noise ratio and partial information loss. Conclusions: The developed simulation framework shows the feasibility of the proposed approach for assessment of arterial compliance by combining multiple data sources. This represents a first step towards integration of arterial compliance measurements in hemodynamic monitoring using existing clinical technology. The Bayesian approach is of particular relevance for such patient monitoring settings, where measurements are repeated frequently, context is relevant, and data is affected by artefacts. In addition, the simulation framework is necessary for future clinical-study design, in order to determine device specifications and the extent to which noise affects the inference process.