rule for adding nested sums with a common index

I've just started learning information theory and have stumbled upon a roadblock due to my apparently not understanding how summation works. I do not understand why this is true:

$- \sum\limits_{i,j}^{} p(x_i, y_j) \log p(y_j | x_i) - \sum\limits_{i,k}^{} p(x_i, z_k) \log p(z_k | x_i) = - \sum\limits_{i,j,k}^{} p(x_i, y_j, z_k) [ \log p(y_j | x_i) + \log p(z_k | x_i) ]$

Can someone explain the justification for this equality? My book is less than forthcoming and I'm not even sure what to search for.

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Is it the case that $p(x_i,y_j)=p(x_i,z_k)=p(x_i,y_j,z_k)$ by any chance? Or perhaps $p(x_i,y_j,z_k)$ is explicitly defined to be $\dfrac{p(x_i,y_j)\log p(y_j|x_i)+p(x_i,z_k)\log p(z_k|x_i))}{\log p(y_j|x_i)+\log p(z_k|x_i)}$? Otherwise it doesn't make much sense to me either. – Rahul Jan 26 '13 at 22:47
No, none of the probabilities have explicit definitions. I keep checking the definitions and I can't escape the feeling that I'm missing something... The claim is that $H(Y|X) + H(Z|X) = -\sum\limits_{i,j,k} p(x_i, y_j, z_k)[\log p(y_j |x_i) + \log p (z_k | x_i)]$ and from here I replace $H(Y|X)$ and $H(Z|X)$ with the definition $H(Y|X) = - \sum\limits_{i,j} p(x_i, y_j) \log p(y_i|x_i)$. – evenex_code Jan 26 '13 at 22:58

$\sum_{i,j,k} p(x_i, y_j, z_k) f(x_i, y_j) = \sum_{i,j} p(x_i, y_j) f(x_i, y_j)$