  The paper introduces a generalization for known probabilistic models such as
log-linear and graphical models, called here multiplicative models. These
models, that express probabilities via product of parameters are shown to
capture multiple forms of contextual independence between variables, including
decision graphs and noisy-OR functions. An inference algorithm for
multiplicative models is provided and its correctness is proved. The complexity
analysis of the inference algorithm uses a more refined parameter than the
tree-width of the underlying graph, and shows the computational cost does not
exceed that of the variable elimination algorithm in graphical models. The
paper ends with examples where using the new models and algorithm is
computationally beneficial.
