Posted by Julie on February 20, 2001 at 01:00:31:
I also want to make sure I am completely visualizing the problem correctly.
Could you tell me if my following ideas for implementation and all my
assumptions are correct?
-The input is an .xml file and a .dat file which contains k cases modeling
the same Bayesian network.
-I am also given which nodes in the network are true.
-In a given phase, I take in a case from a line in the .dat file. Whichever
values it has for my evidence nodes I hold constant, and then try to predict
the outcome of all other (query) nodes.
-To predict the values of these query nodes, I loop through all possible
values that each query node can take, evaluate the probability of each
possible value based on its parents, and then choose the value corresponding
to the maximal probability as my prediction. I continue this until all
query nodes have been predicted.
-I then count each error (where an error is a difference between my
prediction and the actual value in the line in the .dat file), and calculate
the inferential loss.
If my above ideas are correct, I have the following questions on how to
implement them:
-Where does the dataGenerate function come in from DataGen? It seems to me
that if I randomly generate all query root nodes, then I would then be able
to infer all other query nodes by simply taking the max probability
associated with the given parents. Or does the dataGenerate generate
appropriate values while taking the parent's values into account?
-How do I access the probability table? I know that each node has a Vector
called probabilitytable associated with it, but how do I tell from this
which values correspond to a certain instantiation of the node's parents?
-I believe I'm supposed to randomly generate (using dataGenerate) values for
the query nodes over and over until a certain value is "honed in on". Does
this correspond to the place in Dagum and Luby's algorithm where they loop
till the sum over all instantiations of the product of probabilites that
that instantiation will have given its parents until that sum is greater
than the specified S*?
If at all possible, I'd really like the method and class specifications that
you mentioned as well as a somewhat layman's explanation as to how my module
basically operates, and how exactly it makes use of all the classes and
methods from the data generator.
I'm sorry I keep having various confusions pop up, but I want to make sure
that I fully understand what I am trying to do and how best to go about it
before attacking things any more. I feel like I have a feel for the basic
ideas behind the module, but am still unclear on the more detailed
specifications of implementation. I'd really like to get this module
finished this week, so your comments are greatly appreciated.