Search Results for author: Tom S. Verma

Found 4 papers, 1 papers with code

d-Separation: From Theorems to Algorithms

no code implementations27 Mar 2013 Dan Geiger, Tom S. Verma, Judea Pearl

The algorithm runs in time O (l E l) where E is the number of edges in the network.

On the Equivalence of Causal Models

no code implementations27 Mar 2013 Tom S. Verma, Judea Pearl

Scientists often use directed acyclic graphs (days) to model the qualitative structure of causal theories, allowing the parameters to be estimated from observational data.

Causal Networks: Semantics and Expressiveness

no code implementations27 Mar 2013 Tom S. Verma, Judea Pearl

Dependency knowledge of the form "x is independent of y once z is known" invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies.

Deciding Morality of Graphs is NP-complete

1 code implementation6 Mar 2013 Tom S. Verma, Judea Pearl

In order to find a causal explanation for data presented in the form of covariance and concentration matrices it is necessary to decide if the graph formed by such associations is a projection of a directed acyclic graph (dag).

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