The site notes that probability models are important methods for representing uncertainty, and mentions that various probabilistic frameworks include Bayesian networks, hidden Markov models, and stochastic context-free languages, along with other popular tools for describing appropriate scenarios exhibiting uncertainty. It notes that these types of models have been applied to problems in diagnosis, forecasting, automated vision, sensor fusion, manufacturing control, speech recognition, and computational biology. However, traditional approaches have a major drawback-they have a rigid structure, and therefore, lack of versatility in representing complex models.