Computational imaging is a rapidly growing area that seeks to enhance the capabilities of imaging instruments by viewing imaging as an inverse problem. Plug-and-Play Priors (PnP) is one of the most popular frameworks for solving computational imaging problems through integration of physical and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods to provide state-of-the-art imaging algorithms. PnP models alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned image prior in the form of an artifact reducing deep neural network. This talk presents a principled discussion of PnP under inexact physical and learned models. Inexact models arise naturally in computational imaging when using approximate physical models for efficiency or when test images are from a different distribution than images used for training the image prior. We present several successful applications of our theoretical and algorithmic insights in bio-microscopy, computerized tomography, and magnetic resonance imaging.