Evidence Absorption and Propagation through Evidence Reversals
This work provides a theoretical insight into inference algorithms for probabilistic models, but it is incremental as it builds on established methods.
The paper extends probabilistic inference in belief networks by introducing evidence reversal to compute posterior joint distributions, showing that three existing algorithms are identical for forest-structured networks.
The arc reversal/node reduction approach to probabilistic inference is extended to include the case of instantiated evidence by an operation called "evidence reversal." This not only provides a technique for computing posterior joint distributions on general belief networks, but also provides insight into the methods of Pearl [1986b] and Lauritzen and Spiegelhalter [1988]. Although it is well understood that the latter two algorithms are closely related, in fact all three algorithms are identical whenever the belief network is a forest.