Focused Inference
We develop a method similar to variable elimination for computing approximate marginals in graphical models. An underlying notion in this method is that it is not always necessary to compute marginals over all the variables in the graph, but focus on a few variables of interest. The Focused Inference (FI) algorithm introduced reduces the original distribution to a simpler one over the variables of interest. This is done in an iterative manner where in each step the operations are guided by (local) optimality properties. We exemplify various properties of the focused inference algorithm and compare it with other methods. Numerical simulation indicates that FI outperform competing methods.