Zobrazeno 1 - 10
of 46 002
pro vyhledávání: '"DAGs"'
Publikováno v:
Chinese Journal of Stroke. Oct2024, Vol. 19 Issue 10, p1221-1229. 9p.
Autor:
Hellmuth, Marc, Lindeberg, Anna
We explore the connections between clusters and least common ancestors (LCAs) in directed acyclic graphs (DAGs), focusing on DAGs with unique LCAs for specific subsets of their leaves. These DAGs are important in modeling phylogenetic networks that a
Externí odkaz:
http://arxiv.org/abs/2411.14057
Autor:
Lindeberg, Anna, Hellmuth, Marc
Rooted phylogenetic networks, or more generally, directed acyclic graphs (DAGs), are widely used to model species or gene relationships that traditional rooted trees cannot fully capture, especially in the presence of reticulate processes or horizont
Externí odkaz:
http://arxiv.org/abs/2411.00708
Autor:
Arav, Marina, van der Holst, Hein
Given a digraph $D=(V,A)$ with vertex-set $V=\{1,\ldots,n\}$ and arc-set $A$, we denote by $Q(D)$ the set of all real $n\times n$ matrices $B=[b_{u,w}]$ with $b_{u,u}\not=0$ for all $u\in V$, $b_{u,w} \not= 0$ if $u\not=w$ and there is an arc from $u
Externí odkaz:
http://arxiv.org/abs/2410.12002
Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability t
Externí odkaz:
http://arxiv.org/abs/2410.14485
Autor:
Guo, Anna, Nabi, Razieh
The identification theory for causal effects in directed acyclic graphs (DAGs) with hidden variables is well-developed, but methods for estimating and inferring functionals beyond the g-formula remain limited. Previous studies have proposed semiparam
Externí odkaz:
http://arxiv.org/abs/2409.03962
Autor:
Neuwohner, Meike
The Maximum Leaf Spanning Arborescence problem (MLSA) is defined as follows: Given a directed graph $G$ and a vertex $r\in V(G)$ from which every other vertex is reachable, find a spanning arborescence rooted at $r$ maximizing the number of leaves (v
Externí odkaz:
http://arxiv.org/abs/2407.04342
There has been a growing interest in causal learning in recent years. Commonly used representations of causal structures, including Bayesian networks and structural equation models (SEM), take the form of directed acyclic graphs (DAGs). We provide a
Externí odkaz:
http://arxiv.org/abs/2406.15229
Causal interactions among a group of variables are often modeled by a single causal graph. In some domains, however, these interactions are best described by multiple co-existing causal graphs, e.g., in dynamical systems or genomics. This paper addre
Externí odkaz:
http://arxiv.org/abs/2406.08666