In many image processing, computer vision, and pattern recognition applications, there is often a large degree of uncertainty associated with factors such as the appearance of the underlying scene within the acquired data, the location and trajectory of the object of interest, the physical appearance (e.g., size, shape, color, etc.) of the objects being detected, etc. Given these uncertainties, there can be a wide range of possible outcomes for each of these factors that cannot be accounted for using deterministic approaches in an efficient or effective manner. Stochastic models, on the other hand, allow such uncertainties to be taken into account to provide a more complete picture and a robust representation of the problem at hand. Researchers in the VIP lab are investigating novel approaches for constructing robust, large-scale stochastic models to better tackle image processing and computer vision problems such as image denoising, segmentation, registration, and classification in an robust and efficient manner.