Algorithm 6.2's notation confuses me a little:

What does $i$ represent here? How do I read $i:j \in Pa(...)$?

Thanks

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Algorithm 6.2's notation confuses me a little:

\sum_{i:j \in Pa(u^{(i)})} grad\_table[u^{(i)}]\frac{\partial u^{(i)}}{\partial u^{(j)}}

What does $i$ represent here? How do I read $i:j \in Pa(...)$?

Thanks

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John, I also find this section less clear. However, this is what I think the book meant: probability distributions can be described by parameters, e.g. univariate Gaussian PDF is defined by its mean and variance. When we try to learn about a Gaussian distribution from data, we try to learn both of these parameters, variance, like mean, is just a parameter. I agree that some concrete examples would have been helpful.

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