Perturb-and-MAP Random Fields

Probabilistic Bayesian methods such as Markov random fields are well suited for modeling structured data, providing a natural conceptual framework for capturing the uncertainty in interpreting them and automatically learning model parameters from training examples. However, Bayesian methods are often computationally too expensive for large-scale applications compared to deterministic energy minimization techniques.