Effective layout pattern sampling is a fundamental component for lithography process optimization, hotspot detection, and model calibration. Existing pattern sampling algorithms rely on either vector quantization or heuristic approaches. However, it is difficult to manage these methods due to the heavy demands of prior knowledge, such as high-dimensional layout features and manually tuned hypothetical model parameters. We present a self-contained layout pattern sampling framework, where no manual parameter tuning is needed. To handle high dimensionality and diverse layout feature types, we propose a nonlinear dimensionality reduction technique with kernel parameter optimization. Furthermore, we develop a Bayesian model-based clustering, through which automatic sampling is realized without arbitrary setting of model parameters. The effectiveness of our framework is verified through a sampling benchmark suite and two applications: lithography hotspot detection and optical proximity correction.