The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexample where MP converges to a suboptimal fixed point (Kulesza & Pereira, 2008). In this work, we s...

Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm, or loopy belief-propagation. The exact solution to this problem is well known to be exponential in the size of the model’s maximal cliques after it is triangulated, while approximate inference is typically exponential in the size of the model’s factors. In this...

Many tasks in probabilistic reasoning can be cast as max-sum-product problems, a hard class of combinatorial problems. We describe our results in obtaining a new approximation scheme for the problem, that can be turned into an anytime procedure. For many tasks, this scheme can be shown to be asymptotically the best possible heuristic.

The purpose of this survey is to present some approximation and shape preserving properties of the so-called nonlinear (more exactly sublinear) and positive, max-product operators, constructed by starting from any discrete linear approximation operators, obtained in a series of recent papers jointly written with B. Bede and L. Coroianu. We will present the main results for the max-product opera...

changes in proximate composition, colour and textural characteristics of ozone-processed shrimp as affected by combined effects of increasing ozone exposures and iced storage were investigated and for the first time. whilst proximate composition comprised of fat, moisture, and protein contents and colour comprised of l*, a*, b* and metric chroma (c), the texture comprised of adhesiveness, fract...

In this lecture, we study the Belief propagation algorithm(BP) and the Max Product algorithm(MP). Last lecture reminds us of that in MRF, computing the marginal probabilities of random variables and Maximum A Posteriori(MAP) assignment is important. The Belief Propagation algorithm is a popular algorithm that is used to compute marginal probability of random variables. Max Product algorithm is ...

Stripper columns are used for sweetening crude oil, and they must hold product hydrogen sulfide content as near the set points as possible in the faces of upsets. Since product    quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is n...

Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in ...