%0 Report %1 citeulike:142938 %A Heckerman, D. %C Redmond, Washington %D \# 1995 %K bayesian, learning, machine, networks, probabilistic %T A tutorial on learning with bayesian networks %U http://citeseer.ist.psu.edu/41127.html %X A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to...

@techreport{citeulike:142938, abstract = {A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to...}, added-at = {2008-06-17T16:01:02.000+0200}, address = {Redmond, Washington}, author = {Heckerman, D.}, biburl = {https://www.bibsonomy.org/bibtex/2b9b2b0573b14988138ce39d6f829ba2f/pprett}, citeulike-article-id = {142938}, comment = {at least partially...}, institution = {Microsoft Research}, interhash = {114c932c4e88ddfcc44e4557bc02505e}, intrahash = {b9b2b0573b14988138ce39d6f829ba2f}, keywords = {bayesian, learning, machine, networks, probabilistic}, posted-at = {2007-11-18 14:31:53}, priority = {0}, timestamp = {2008-06-17T16:01:44.000+0200}, title = {A tutorial on learning with bayesian networks}, url = {http://citeseer.ist.psu.edu/41127.html}, year = {\# 1995} }