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Sylvia Nagl [4]Sylvia B. Nagl [1]
  1. Sylvia Nagl, Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer.
    Cancer treatment decisions should be based on all available evidence. But this evidence is complex and varied: it includes not only the patient’s symptoms and expert knowledge of the relevant causal processes, but also clinical databases relating to past patients, databases of observations made at the molecular level, and evidence encapsulated in scientific papers and medical informatics systems. Objective Bayesian nets offer a principled path to knowledge integration, and we show in this chapter how they can be applied to integrate (...)
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  2. Chih-Chun Chen, Sylvia B. Nagl & Christopher D. Clack (2009). A Formalism for Multi-Level Emergent Behaviours in Designed Component-Based Systems and Agent-Based Simulations. In Ma Aziz-Alaoui & C. Bertelle (eds.), From System Complexity to Emergent Properties. Springer. 101--114.
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  3. Sylvia Nagl (2009). Spaces of Affinity. Technoetic Arts: A Journal of Speculative Research 7 (2):191-197.
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  4. Sylvia Nagl, Matthew Williams, Nadjet El-Mehidi, Vivek Patkar & Jon Williamson, Objective Bayesian Nets for Integrating Cancer Knowledge: A Systems Biology Approach.
    According to objective Bayesianism, an agent’s degrees of belief should be determined by a probability function, out of all those that satisfy constraints imposed by background knowledge, that maximises entropy. A Bayesian net offers a way of efficiently representing a probability function and efficiently drawing inferences from that function. An objective Bayesian net is a Bayesian net representation of the maximum entropy probability function. In this paper we apply the machinery of objective Bayesian nets to breast cancer prognosis. Background knowledge (...)
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