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...
Approximating pairwise, or k-wise, independence with sublinear memory is of considerable importance in the data stream model. In the streaming model the joint distribution is give...
In this work we consider the task of relaxing the i.i.d assumption in online pattern recognition (or classification), aiming to make existing learning algorithms applicable to a ...
We consider the problem of finding a maximum independent set in a random graph. The random graph G, which contains n vertices, is modelled as follows. Every edge is included inde...
The construction of causal graphs from non-experimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the gr...