In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...
In this work, we consider the problems of testing whether a distribution over {0, 1}n is k-wise (resp. ( , k)-wise) independent using samples drawn from that distribution. For the...
Noga Alon, Alexandr Andoni, Tali Kaufman, Kevin Ma...
In this paper we present a method of computing the posterior probability of conditional independence of two or more continuous variables from data, examined at several resolutions...
A discrete distribution D over Σ1 × · · · × Σn is called (non-uniform) k-wise independent if for any set of k indexes {i1, . . . , ik} and for any z1 ∈ Σi1 , . . . , zk ...