HONcode

This website is certified by Health On the Net Foundation. Click to verify. This site complies with the HONcode standard for trust worthy health information:
verify here.


Search only trustworthy HONcode health websites:

Affiliations

REACTION is affilliated with these programs and organisations:

REACTION is delivering better and more efficient healthcare services in Europe, thus supporting the Commissions activities in ICT for Health: eHealth.



The REACTION platform allows for the creation of inclusive applications with accessibility for all. The project supports the Commissions campaign: eInclusion - be part of it!



Sign In

Enter Username

Password



Forgotten your password?
Request a new one here.

Facts



The REACTION project is a 4-year project started in 2010. It is partly funded by the European Commission under the 7th Framework Programme in the area of Personal Health Systems under Grant Agreement no. 248590

 Impressum   Privacy

Previous Newsletters


Read previous issues of our newsletter here:

April 2014
August 2013
June 2012
August 2011
April 2011
November 2010

Share this

Bookmark and Share

eHealth at Facebook

Join the discussion on EC eHealth at Facebook!

Downloads: Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral

Downloads Home > Scientific conference papers > Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral

Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral


Abstract:
We consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to directed paths in a causal model. This type of knowledge naturally arises from experimental data, among others. Specifically, we consider the formalisms of Causal Bayesian Networks and
Maximal Ancestral Graphs and their Markov equivalence classes: Partially Directed Acyclic Graphs and Partially Oriented Ancestral Graphs. We introduce sound and complete procedures which are able to incorporate causal prior knowledge in such models. In simulated experiments, we show that often considering even a few causal facts leads to a significant number of new inferences. In a case study, we also show how to use real experimental data to infer causal knowledge and incorporate it into a real biological causal
network. The code is available at mensxmachina.org.
Created:
Admin
23. May 2012 09:52
Downloads
Download 3498
Info
License: ICML, free with reference
O/S: pdf

Download:
Download
Download Stats Downloads: 113
Downloaded: 379008
Most Downloaded: REACTION brochure [ 29902 ]
Most Recent: REACTION brochure [ 29902 ]