The aim of this thesis is to provide tools for the global analysis of nondeterministic dynamical systems with competing attractors considering parameter uncertainty and noise and apply them to real-world engineering problems. For this, an adaptative phase-space discretization strategy based on the classical Ulam method is proposed. Initially, a review of the mathematical definitions of dynamical systems, parametric uncertainty, and noise is presented, and the effect of randomness on the global dynamical structures is highlighted. Discretized transfer operators with the necessary modifications due to parameter uncertainty are derived. The stochastic basin of attraction and attractors’ distributions replace the usual basin and attractor concept. For parameter uncertainty cases, the phase-space is augmented with the corresponding probability space, resulting in a collection of transfer operators for which mean results are obtained. Two adaptative phase-space discretization strategies are proposed, one which only considers the attractors’ distribution and stochastic basins, and another that discretizes the stable and unstable manifolds. The first method is initially applied to the Helmholtz and Duffing oscillators under harmonic or parametric excitation with uncertain parameters or added load noise. They demonstrate the adaptive capabilities of the proposed methods, increasing the quality without overly increasing the computational cost. The time-dependency of stochastic responses is demonstrated, with long-transients influencing the global behavior. Finally, the effect of uncertainties and noise on the basins areas, attractors distributions, and basin boundaries are discussed, which can be used to evaluate the dynamic integrity of the competing basins. Then, two electrically actuated Microelectromechanical Systems (MEMS), an imperfect microcantilever and microarch, are investigated. The effect of added noise and parametric uncertainty on both structures is demonstrated. The results highlight the importance of randomness on the global dynamics of dynamical systems with competing attractors.