I think that I understand the fundamentals of a Bayesian network and am trying to put that into practice by making a sample one, its size being about 20 nodes. But I'm struggling to see how to assign probabilities to all the nodes in such a way that they all satisfy the Markov condition.
The actual probabilities I'd be assigning to my nodes have no importance beyond my requirement that they meet this condition - I'm not building this off of any particular data at the moment, I'm just trying to play around with a Bayesian network. Meaning can come later. So I have a great deal of flexibility. But I'm not sure how to come up with them in a way that meets the condition, without spending a dozen hours carefully checking each conditional probability. Could someone give me some insight? Am I missing something obvious that would make this trivial? Or is it genuinely extremely difficult to make Bayesian networks with more than a few nodes?
I'm at the end of Chapter 1 of Richard Neapolitan's "Learning Bayesian Networks", if that helps.
Thanks in advance to anyone who decides to respond!