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I'm trying to understand how to calculate the strength of every arc in a Bayesian Network.

I came across this report Measuring Connection Strengths and Link Strengths in Discrete Bayesian Networks, but I got lost in the calculation.

In particular, how are the values of Link Strength true, Link Strength blind, and Mutual Information computed in Table 1?

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The $MI$ is easy enough. For $MI(X \to Z)$, we get $U([0.5, 0.5]) - 0.5 U([0.9, 0.1]) - 0.5 U([0.1, 0.9])$. The $[0.5, 0.5]$ comes from $0.5 [0.9, 0.1] + 0.5[0.1, 0.9]$ -- this is how to calculate $P(Z)$ from $P(Z|X)$ and $P(X)$. For $MI(X \to Y)$, we need to marginalize out $Z$. This takes a bit of work, but is not too hard. I manually verified all of the reported values in the figure, and they are all correct.

The $\mathrm{LS}{\mathrm{true}}$ values are a bit trickier, but still feasible. I managed to get the same values. It is important when calculating $\mathrm{LS}{\mathrm{true}}(X \to Z)$ that $Y$ is not a parent, so it does not come into it -- which is why the MI gives exactly the same value.

The "blind" version took me a bit more effort to implement, but again, I am getting the same values as reported in the table. I used the "simple formula" at the top of page 5.

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  • $\begingroup$ Thank you very much @Robby, it helps me understand some part. I don't have a strong mathematical background, which makes me struggle to translate the formulas and numbers in the paper. I'm doing a python code for this formula, and still figure out which variables need to hold which numbers. So please apologize. When you mentioned $MI(X \to Z)$ = $U([0.5,0.5])−0.5U([0.9,0.1])−0.5U([0.1,0.9])$, it does use formula (2) $U(Z)-U(Z|X)$, right? There is also a formula $\sum\limits_{x,y}P(x,y) log_{2} (\frac{P(x,y)}{P(x)P(y)})$, and I'm not sure how to derive numbers for $P(x,y), P(x), P(y)$. $\endgroup$
    – qillbel
    Commented Aug 27, 2020 at 4:39
  • $\begingroup$ Yes, I used formula (2) for that. $P(x,y) = P(y | x) \cdot P(x)$. You might want to take an introduction to probability theory, make sure it covers things up to conditional probabilities, entropy and Bayes' theorem; you'll encounter those a lot. $\endgroup$
    – Robby Goetschalckx
    Commented Aug 27, 2020 at 4:48
  • $\begingroup$ Thanks @Robby, I will look at those materials asap. I have quickly tried to find the numbers for: $P(z,x)=0.5; P(x)=0,5; P(z)=0.5$. I got it from $P(z,x)$ = the average of $P(z|x=true)$ and $P(z|x=false)$, which yield 0.5. $P(x) = P(x=true) = 0.5$. $P(z=true)$ = the average of 0.9 and 0.1 = 0.5. But the result is different from the table when I plug in those number in the formula. I believe I calculated those wrongly, but it will help me to also understand the materials that you suggested if I know where my mistakes are. Thanks again Robby and I will update my understanding. $\endgroup$
    – qillbel
    Commented Aug 27, 2020 at 5:17
  • $\begingroup$ If $P(z|x) = 0.5$ and $P(x) = 0.5$, then $P(x, z) = 0.5 \cdot 0.5 = 0.25$. $\endgroup$
    – Robby Goetschalckx
    Commented Aug 27, 2020 at 14:38
  • $\begingroup$ The calculation you did ($P(z=\mathrm{True}|x=\mathrm{True}) P(x=\mathrm{True}) + P(z =\mathrm{True}| x=\mathrm{False})$ gives us $P(z=\mathrm{True})$, not $P(z, x)$. $\endgroup$
    – Robby Goetschalckx
    Commented Aug 27, 2020 at 14:49

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