Quote Originally Posted by penagate View Post
Don't confuse belief with knowledge.
Do you believe your knowledge?

From Wikipedia,
Belief propagation
Belief propagation is a message passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. It is an inherently Bayesian procedure, which calculates the marginal distribution for each unobserved node, conditional on any observed nodes. Belief propagation is commonly used in artificial intelligence and information theory and has demonstrated empirical success in numerous applications including low-density parity-check codes, turbo codes, free energy approximation, and satisfiability.

The algorithm was first proposed by Judea Pearl in 1982,[1] who formulated this algorithm on trees, and was later extended to polytrees.[2] It has since been shown to be a useful approximate algorithm on general graphs.[3]

If X=(Xv) is a set of discrete random variables with a joint mass function p, the marginal distribution of a single Xi is simply the summation of p over all other variables: