# Bayesian network learning can be derived from learning each conditional distribution separately?

Please helm I am wondering if we consider a learning the parameter of a bayesian network ,with a training set ,where each training set is a vector of values containing all the random variable ,in the network .,Now can we show that the learning parameter of the network according to the MLE ,can be derived by seperately learning the parameters of each conditional distribution

• I'm assuming the conditions are completely disjoint and the frequency of each condition is known in advance? – barrycarter Nov 8 '14 at 16:46
• yes probability of the marginal distributions are known – Susmita Ghosh Nov 13 '14 at 10:00