  Arriving at the complete probabilistic knowledge of a domain, i.e., learning
how all variables interact, is indeed a demanding task. In reality, settings
often arise for which an individual merely possesses partial knowledge of the
domain, and yet, is expected to give adequate answers to a variety of posed
queries. That is, although precise answers to some queries, in principle,
cannot be achieved, a range of plausible answers is attainable for each query
given the available partial knowledge. In this paper, we propose the
Multi-Context Model (MCM), a new graphical model to represent the state of
partial knowledge as to a domain. MCM is a middle ground between Probabilistic
Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we
discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to
form partial beliefs regarding a domain in a gradual and probabilistically
consistent way, and (ii) how to perform inference, i.e., to evaluate a
probability of interest involving some variables of the domain.
