Latest addition : 29 June 2010.
Probabilistic graphical models (PGMs) 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.
This models has been extended, on one hand, 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).
The DyNaMo project will concentrate its efforts on the probabilistic dynamic graphical models and its applications on medicine and industry.
The main objectives are: