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Is H(rule) really necessary? #11
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Hi, it is really a nice paper. But I have a question.
May I know why you need to use Eq. (7) and (8) to approximate the posterior?
My idea is that in the E-step, you need to identify k rules with the best quality.
Since 
you can simply calculate prior x likelihood for each rule in z_hat, and choose the top-k rules.
But what you do is to calculate the H(rule) for each rule and choose the top-k rules.
Since H(rule) is a approximate of the posterior distribution which is proportion to prior x likelihood, it will have the same effect as using prior x likelihood.
It seems to me that all the proof and proposition in Section 3.3 E-step is unnecessary.
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mnqu commentedon May 2, 2022
Thanks for your interests, and this is a good question.
The reason is that z_I here is a set of logic rules, so the prior and posterior are defined on a set of logic rules, rather than a single logic rule. Therefore, we propose to use approximation inference to infer the posterior, where H(rule) is calculated.
Does it make sense to you?