Inference for Multiplicative Models
This work offers a new modeling framework for probabilistic inference, but appears incremental as it builds directly on existing graphical models.
The paper introduces multiplicative models as a generalization of probabilistic models like log-linear and graphical models, showing they capture various forms of contextual independence. It provides a correct inference algorithm with complexity analysis demonstrating computational benefits in some cases.
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.