  Belief fusion is the principle of combining separate beliefs or bodies of
evidence originating from different sources. Depending on the situation to be
modelled, different belief fusion methods can be applied. Cumulative and
averaging belief fusion is defined for fusing opinions in subjective logic, and
for fusing belief functions in general. The principle of fission is the
opposite of fusion, namely to eliminate the contribution of a specific belief
from an already fused belief, with the purpose of deriving the remaining
belief. This paper describes fission of cumulative belief as well as fission of
averaging belief in subjective logic. These operators can for example be
applied to belief revision in Bayesian belief networks, where the belief
contribution of a given evidence source can be determined as a function of a
given fused belief and its other contributing beliefs.
