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  1. Causality in medicine with particular reference to the viral causation of cancers.Brendan Clarke - 2011 - Dissertation, University College London
    In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I argue (...)
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  • On the computational complexity of ethics: moral tractability for minds and machines.Jakob Stenseke - 2024 - Artificial Intelligence Review 57 (105):90.
    Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...)
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  • Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks.Weixiong Zhang, Guandong Wang, Zhao Xing & Lars Wittenburg - 2005 - Artificial Intelligence 161 (1-2):55-87.
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  • Distributed revision of composite beliefs.Judea Pearl - 1987 - Artificial Intelligence 33 (2):173-215.
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  • Assumptions, beliefs and probabilities.Kathryn Blackmond Laskey & Paul E. Lehner - 1989 - Artificial Intelligence 41 (1):65-77.
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  • Gibbs sampling in Bayesian networks.Tomas Hrycej - 1990 - Artificial Intelligence 46 (3):351-363.
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  • Fusion and propagation with multiple observations in belief networks.Mark A. Peot & Ross D. Shachter - 1991 - Artificial Intelligence 48 (3):299-318.
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  • Connectionist learning of belief networks.Radford M. Neal - 1992 - Artificial Intelligence 56 (1):71-113.
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  • Use of the Gibbs sampler in expert systems.Jeremy York - 1992 - Artificial Intelligence 56 (1):115-130.
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  • Belief networks revisited.Judea Pearl - 1993 - Artificial Intelligence 59 (1-2):49-56.
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  • Approximating probabilistic inference in Bayesian belief networks is NP-hard.Paul Dagum & Michael Luby - 1993 - Artificial Intelligence 60 (1):141-153.
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  • Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks.David Poole - 1996 - Artificial Intelligence 88 (1-2):69-100.
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  • Tail uncertainty analysis in complex systems.Enrique Castillo, Cristina Solares & Patricia Gómez - 1997 - Artificial Intelligence 96 (2):395-419.
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  • An optimal approximation algorithm for Bayesian inference.Paul Dagum & Michael Luby - 1997 - Artificial Intelligence 93 (1-2):1-27.
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