Multinomial selection is concerned with selecting the most probable (best) multinomial alternative. The alternatives compete in a number of independent trials. In each trial, each alternative wins with an unknown probability specific to that alternative. A long-standing research goal has been to find a procedure that minimizes the expected number of trials subject to a lower bound on the probab...

Probabilistic databases (PDBs) have been widely studied in the literature, as they form the foundations of large-scale probabilistic knowledge bases like NELL and Google’s Knowledge Vault. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information...

Loopy belief propagation (BP) has been successfully used in a number of difficult graphical models to find the most probable configuration of the hidden variables. In applications ranging from protein folding to image analysis one would like to find not just the best configuration but rather the top M . While this problem has been solved using the junction tree formalism, in many real world pro...

An algorithm is presented for nding the most probable image motion between two images from fuzzy point correspondences. In fuzzy correspondence a point in one image is assigned to a region in the other image. Such a region can be line (aperture eeect) or a probability matrix. Noise and outliers are always present, and points may come from diierent motions. The presented algorithm, which uses li...

Abstract Typically, when one discusses approximation algorithms for (NP-hard) problems (like TRAVELING SALESPERSON, VERTEX COVER, KNAPSACK), one refers to algorithms that return a solution whose value is (at least ideally) close to optimal; e.g., a tour with almost minimal length, a vertex cover of size just above minimal, or collection of objects that has close to maximal value. In contrast, o...