Hi Xiangdong,
I can think of at least two ways to interpret what you are trying to do,
here is the answer for all three interpretations:
(1) I want to generate a random JPD along with its perfect map D.
To do this, it is sufficient to construct a random dag and randomly set the
parameters so that no two columns in a given table are identical. The dag
will be a perfect map to the JPD generated by the network.
(2) Given a JPD I want to construct its perfect map D.
As far as I know, the only way to do this is to query the JPD for
independence relations along the lines of a constraint-based learning
algorithm, for example the PC algorithm (given in the book "Causation,
Prediction and Search", Spirtes, Glymour and Scheines), or Pearl and Verma's
algorithm (http://citeseer.nj.nec.com/pearl91theory.html).
Hope this helps,
Denver.
---- Denver Dash http://www.sis.pitt.edu/~ddash----- Original Message ----- From: "Xiangdong An" <xdan@cis.uoguelph.ca> To: <uai@cs.orst.edu> Sent: Wednesday, September 26, 2001 10:02 AM Subject: [UAI] JPD for a DAG such that DAG is p-map
> Hi everybody, > > I am wodering if I generate a probabilisty distribution JPD > by generating a set of conditional probability distributions > {P(Xi|II(Xi))} for a DAG D on a topological ordering X1,X2,...,Xn > such that II(Xi) is the minimum set of predecessors satisfying > P(Xi|II(Xi))=P(Xi|X1,...,Xi-1). > > Then the DAG D is the perfect map of the JPD? If not, there > is any practical way to generate them? > > Xiangdong > >
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