Causal inference and causal explanation with background knowledge
C Meek - arXiv preprint arXiv:1302.4972, 2013 - arxiv.org
This paper presents correct algorithms for answering the following two questions;(i) Does
there exist a causal explanation consistent with a set of background knowledge which …
there exist a causal explanation consistent with a set of background knowledge which …
Directed cyclic graphical representations of feedback models
PL Spirtes - arXiv preprint arXiv:1302.4982, 2013 - arxiv.org
The use of directed acyclic graphs (DAGs) to represent conditional independence relations
among random variables has proved fruitful in a variety of ways. Recursive structural …
among random variables has proved fruitful in a variety of ways. Recursive structural …
Search for third generation scalar leptoquarks in pp collisions at TeV with the ATLAS detector
G Aad, T Abajyan, B Abbott, J Abdallah… - Journal of High Energy …, 2013 - Springer
A bstract A search for pair-produced third generation scalar leptoquarks is presented, using
proton-proton collisions at\(\sqrt {s}= 7\) TeV at the LHC. The data were recorded with the …
proton-proton collisions at\(\sqrt {s}= 7\) TeV at the LHC. The data were recorded with the …
A polynomial-time algorithm for deciding Markov equivalence of directed cyclic graphical models
TS Richardson - arXiv preprint arXiv:1302.3600, 2013 - arxiv.org
Although the concept of d-separation was originally defined for directed acyclic graphs (see
Pearl 1988), there is a natural extension of he concept to directed cyclic graphs. When …
Pearl 1988), there is a natural extension of he concept to directed cyclic graphs. When …
Chain graphs for learning
WL Buntine - arXiv preprint arXiv:1302.4933, 2013 - arxiv.org
Chain graphs combine directed and undirected graphs and their underlying mathematics
combines properties of the two. This paper gives a simplified definition of chain graphs …
combines properties of the two. This paper gives a simplified definition of chain graphs …
Dynamic construction of belief networks
RP Goldman, E Charniak - arXiv preprint arXiv:1304.1092, 2013 - arxiv.org
We describe a method for incrementally constructing belief networks. We have developed a
network-construction language similar to a forward-chaining language using data …
network-construction language similar to a forward-chaining language using data …
Bayesian networks from the point of view of chain graphs
M Studeny - arXiv preprint arXiv:1301.7414, 2013 - arxiv.org
AThe paper gives a few arguments in favour of the use of chain graphs for description of
probabilistic conditional independence structures. Every Bayesian network model can be …
probabilistic conditional independence structures. Every Bayesian network model can be …
Improved learning of Bayesian networks
T Kocka, R Castelo - arXiv preprint arXiv:1301.2283, 2013 - arxiv.org
The search space of Bayesian Network structures is usually defined as Acyclic Directed
Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of …
Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of …
Concepts and a case study for a flexible class of graphical Markov models
N Wermuth, DR Cox - … and Complex Data Structures: Festschrift in Honour …, 2013 - Springer
To observe and understand relations among several features of individuals or objects is one
of the central tasks in many substantive fields of research, including the medical, social …
of the central tasks in many substantive fields of research, including the medical, social …
On the application of discrete marginal graphical models
Graphical models are defined by general and possibly complex conditional independence
assumptions and are well suited to model direct and indirect associations and effects that …
assumptions and are well suited to model direct and indirect associations and effects that …