The motivation for the paper is the geometric approach to learning Bayesian network (BN)
structure. The basic idea of our approach is to represent every BN structure by a certain …

First, we recall the basic idea of an algebraic and geometric approach to learning a
Bayesian network (BN) structure proposed in (Studený, Vomlel and Hemmecke, 2010): to …

We recall the basic idea of an algebraic approach to learning Bayesian network (BN)
structures, namely to represent every BN structure by a certain (uniquely determined) vector …

This paper considers the problem of defining distributions over graphical structures. We
propose an extension of the hyper Markov properties of Dawid and Lauritzen [Ann. Statist …

As is well known, greedy algorithm is usually used as local optimization method in many
heuristic algorithms such as ant colony optimization, taboo search, and genetic algorithms …

The basic idea of an algebraic approach to learning Bayesian network (BN) structures is to
represent every BN structure by a certain uniquely determined vector, called the standard …

The topic of this chapter is conditional independence models. We review mathematical
objects that are used to generate conditional independence models in the area of …

We derive standard imsets for undirected graphical models and chain graphical models.
Standard imsets for undirected graphical models are described in terms of minimal …

Explaining data in a concise and efficient manner has become increasingly important in
today's society. This thesis pertains to the problem of finding causal links within data, and …

A distributed information retrieval of information retrieval is one of the important research
direction. With the rapid growth of WEB information, information is more and more, this …