Calculating joint over arbitrary sets of variables

From: Denver Dash (ddash@isp.pitt.edu)
Date: Thu Mar 30 2000 - 12:15:28 PST

  • Next message: Bruce D'Ambrosio: "Re: Calculating joint over arbitrary sets of variables"

    Hi, this is a repost of a previous thread (BDe scoring metric). I thought I
    would repost it under this title to see if it gets a better response.
    Basically I'm looking for an efficient way to calculate the joint of an
    arbitrary subset of nodes in a Bayes net. Below is one algorithm I
    proposed, but it's not terribly efficient.

    Thanks,
    Denver.

    - ----- Original Message -----
    From: "Denver Dash" <ddash@isp.pitt.edu>
    To: <uai@CS.ORST.EDU>
    Sent: Thursday, March 23, 2000 5:23 PM
    Subject: Re: [UAI] BDe scoring metric

    > Basically you need to calculate the joint over an arbitrary subset of
    > variables: P(V1,V2,...Vm) where m<n (the total number of variables).
    >
    > One way to do this is to do the following:
    >
    > Make use of the fact that P(V1,V2,...,Vm) is equal to
    > P(V1)P(V2|V1)P(V3|V1,V2)...P(Vm|V1,...,Vn-1).
    >
    > This suggests the following algorithm to calculate the joint of a subset S
    > of nodes:
    >
    > Evidence_Set = {};
    > jointProb = 1;
    >
    > For each node V in S do:
    > Update beliefs in network given Evidence_Set.
    > query node V to get P(V|Evidence_Set).
    > jointProb = jointProb * P(V|Evidence_Set)
    > Evidence_Set += V
    >
    > Of course if the nodes are in the same clique then you can find a more
    > efficient way to calculate their joint (marginalize the potentials over
    the
    > non-included variables), but in general this won't be the case.
    >
    > I would also be interested if anybody knows of a more efficient way to do
    > this calculation in general.
    >
    > Cheers,
    > Denver.
    > --
    > Denver Dash http://www.sis.pitt.edu/~ddash
    >
    > ----- Original Message -----
    > From: "Mohamed Bendou" <mohamed@esiea-ouest.fr>
    > To: <uai@CS.ORST.EDU>
    > Sent: Thursday, March 23, 2000 12:58 PM
    > Subject: [UAI] BDe scoring metric
    >
    >
    > > Hi,
    > > I try to program the BDe scoring metric for learning bayesian networks.
    > > Could you indicate me reference to the algorithmic aspects of
    > > calculating priors on parameters?
    > > I do not understand how i can calculate efficiently the prior joint
    > > probabilities
    > > from the prior network.
    > >
    > > Thank you.
    > >
    > >
    > > Mohamed BENDOU
    > >
    > >
    > > --
    > > Mohamed BENDOU
    > >
    > >
    >

    ------- End of Forwarded Message



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