Dear author,
I found there might be a bug in Bayesian network's sample function.
For the above structure, assuming that the distribution list is ['c', 'a', 'b']:
so firstly we will sample for node 'c', but we don't have value of 'a' and 'b',
then we will sample for 'a' and 'b'.
Finally we will not get the value of 'c'
am I right?
Hm, looks like you might be right. This algorithm works if the nodes/features are already topologically sorted but, if they're not, I might need to add a second pass over the loop.
Dear author, I found there might be a bug in Bayesian network's sample function. For the above structure, assuming that the distribution list is ['c', 'a', 'b']: so firstly we will sample for node 'c', but we don't have value of 'a' and 'b', then we will sample for 'a' and 'b'. Finally we will not get the value of 'c' am I right?