It depends on your model - size and variables. If you have a moderately sized model with discrete variables you can infer values using exact algorithms (e.g. cl

It depends on your model - size and variables. If you have a moderately sized model with discrete variables you can infer values using exact algorithms (e.g. clique tree propagation, variable elimination, etc). If your model is too complex or has certain continuous distributions then you can use simulation techniques or other approximate algorithms. Given that probabilistic graphical models is in general NP-hard (or #P-hard) then approximate algorithms are often used. However, for many problems discrete valued networks can be effectively managed with exact algorithms. Some tools for exact inference (that are free or commercial with free versions) are: http://genie.sis.pitt.edu http://genie.sis.pitt.edu http://www.norsys.com/netica.html http://www.norsys.com/netica.html http://www.hugin.com http://www.hugin.com Netica in particular has a large library of example networks.