## Belief Networks Revisited (1994)

Citations: | 36 - 6 self |

### BibTeX

@MISC{Pearl94beliefnetworks,

author = {Judea Pearl},

title = {Belief Networks Revisited},

year = {1994}

}

### Years of Citing Articles

### OpenURL

### Abstract

this paper, Rumelhart presented compelling evidence that text comprehension must be a distributed process that combines both top-down and bottom-up inferences. Strangely, this dual mode of inference, so characteristic of Bayesian analysis, did not match the capabilities of either the "certainty factors" calculus or the inference networks of PROSPECTOR -- the two major contenders for uncertainty management in the 1970s. I thus began to explore the possibility of achieving distributed computation in a "pure" Bayesian framework, so as not to compromise its basic capacity to combine bi-directional inferences (i.e., predictive and abductive) . Not caring much about generality at that point, I picked the simplest structure I could think of (i.e., a tree) and tried to see if anything useful can be computed by assigning each variable a simple processor, forced to communicate only with its neighbors. This gave rise to the tree-propagation algorithm reported in [15] and, a year later, the Kim-Pearl algorithm [12], which supported not only bi-directional inferences but also intercausal interactions, such as "explaining-away." These two algorithms were described in Section 2 of Fusion.