Computing Tricky Probabilities Using Model Counting

&quot;-H1 or -X or -Y = True<p>The highlighted lines are what’s call the Tseitin transformation, which basically means that when H1 is FALSE, (NOT X or NOT y) must be TRUE&quot;<p>Forgive me if I&#x27;m wrong, but I think it should be &quot;when H1 is TRUE, (NOT X or NOT y) must be TRUE&quot; since this is equivalent to &quot;H1 -&gt; NOT (X &amp; Y)&quot;

Complicated causal chains can be tricky to work with, but estimating the probability of a bad event (e.g. nuclear facility meltdown) is extremely important. This article showcases how complicated, inter-dependent causal chains can be analyzed through state-of-the-art propositional model counters.