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Parameter learning behaviour on BN example #98
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Hi Pierre. This seems to be a bug indeed. We'll look into it. |
Thank you for your input! |
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I've noticed a strange result when doing parameter learning, where I get a different result depending on whether the parameter to tune is (a) directly associated with the rule or is (b) associated to a probabilistic fact included in the clauses of the rule.
From the Bayesian network example:
with the following evidence:
In this case, I get P(burglary) = 0.3333, P(alarm1) = 0.5, P(alarm2) = 1 and P(alarm3) = 0.
However, if I try to attach those tunable parameters to the rule directly, I do not the get the same results when running LFI on the same evidence:
P(burglary) is still equal to 0.3333 and P(alarm2) is still equal to 1, but this time the probability of the first alarm is set to 0, even though no case of burglary + alarm was observed in the evidence (so the probability should not be modified from the initial 0.5).
Any idea why this is the case?
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