Re: [UAI] BDe scoring metric

From: Denver Dash (ddash@isp.pitt.edu)
Date: Thu Mar 23 2000 - 14:23:07 PST

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    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
>
>

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