Closed form path integral based approximate solutions of stochastic differential equations

Models incorporating randomness, by allowing for disturbances or random variations over time, are commonly used in a wide range of scientific disciplines. However, the existing numerical methodologies in many cases can be computationally prohibitive. Thus, in this thesis a novel computationally efficient method is developed for approximating the solutions of various models, under a range of conditions, in a closed-form manner.