نتایج جستجو برای: causal networks

تعداد نتایج: 487531  

Journal: :International Journal of Data Science and Analytics 2016

Journal: :CoRR 2008
Sanjiang Li

Markov networks and Bayesian networks are effective graphic representations of the dependencies embedded in probabilistic models. It is well known that independencies captured by Markov networks (called graph-isomorphs) have a finite axiomatic characterization. This paper, however, shows that independencies captured by Bayesian networks (called causal models) have no axiomatization by using eve...

1994
Adnan Darwiche Judea Pearl

For a logical database to faithfully represent our beliefs about the world, one should not only insist on its logical consistency but also on its causal consistency. Intuitively, a database is causally inconsistent if it supports belief changes that contradict with our perceptions of causal influences for example, coming to conclude that it must have rained only because the sprinkler was observ...

2005
P. Białas Z. Burda

Growing networks have a causal structure. We show that the causality strongly influences the scaling and geometrical properties of the network. In particular the average distance between nodes is smaller for causal networks than for corresponding homogeneous networks. We explain the origin of this effect and illustrate it using as an example a solvable model of random trees. We also discuss the...

2007
Kathryn B. Laskey

Intervention theories of causality define a relationship as causal if appropriately specified interventions to manipulate a putative cause tend to produce changes in the putative effect. Interventionist causal theories are commonly formalized by using directed graphs to represent causal relationships, local probability models to quantify the relationship between cause and effect, and a special ...

Journal: :J. Artif. Intell. Res. 2015
Alexander Motzek Ralf Möller

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a “causal design,” i.e., without anticipating indirect influences appropriately in time, we ...

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