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The DyNaMo project was selected for financing by the International Cooperation Programme for the Promotion of Scientific and Technological Research of the European Union and Mexico.
DyNaMo project is based on probabilistic graphical models (PGMs) which allow the representation of probability distributions with many variables in a compact form, also they help to make probabilistic inference (estimate the probability of certain variables given other known) efficiently.
PGMs include: Bayesian classifiers, Bayesian networks, Markov random fields, etc.
PGM´s have many applications in medicine, expert systems, industrial diagnosis, image analysis, robotics and many others.
DyNamo will extend PGM´s to dynamic processes, including hidden Markov models and dynamic Bayesian networks. On the other hand, there are models that include decisions and utilities, such as dynamic influence diagrams (DIDs) and Markov decision processes (MDPs).
