Generating Conditional Probabilities for Bayesian Networks: Easing the
  Knowledge Acquisition Problem
  The number of probability distributions required to populate a conditional
probability table (CPT) in a Bayesian network, grows exponentially with the
number of parent-nodes associated with that table. If the table is to be
populated through knowledge elicited from a domain expert then the sheer
magnitude of the task forms a considerable cognitive barrier. In this paper we
devise an algorithm to populate the CPT while easing the extent of knowledge
acquisition. The input to the algorithm consists of a set of weights that
quantify the relative strengths of the influences of the parent-nodes on the
child-node, and a set of probability distributions the number of which grows
only linearly with the number of associated parent-nodes. These are elicited
from the domain expert. The set of probabilities are obtained by taking into
consideration the heuristics that experts use while arriving at probabilistic
estimations. The algorithm is used to populate the CPT by computing appropriate
weighted sums of the elicited distributions. We invoke the methods of
information geometry to demonstrate how these weighted sums capture the
expert's judgemental strategy.
