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Modeling and simulation have proved over the years to provide insight into system behavior and design. Traditionally, models are designed to only support deterministic inputs and parameters. This forces the user to provide an exact set of operating conditions to run the simulation. Greater insight into the system's behavior can be achieved by allowing inputs and parameters to reflect the user's uncertainty. The Monte Carlo method is a common yet costly approach to generating these desired statistical results. This book introduces an alternative approach by applying polynomial chaos theory to an established circuit modeling method. This results in a general uncertainty-based modeling and simulation framework which yields full system statistics after one simulation run. Linear and non- linear systems are discussed using examples from basic circuits to power converters complete with closed-loop control systems. This book will be useful to anyone interested in modeling and simulation, advanced numerical methods or uncertainty propagation.