We describe an algorithm for approximate inference in graphical models based on
Holder's inequality that provides upper and lower bounds on common summation
problems such as computing the partition function, data likelihood, or marginal
probabilities.  Our algorithm unifies and extends several existing approaches,
including variable elimination techniques such as mini-bucket elimination and
variational methods such as mean field, tree reweighted belief propagation and
conditional entropy approximations.