Uncertainty
Simulation approaches to general probabilistic inference on belief networks
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Weighting and integrating evidence for stochastic simulation in Bayesian networks
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Heuristic search as evidential reasoning
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HUGIN: A shell for building Bayesian belief universes for expert systems
Andersen, S. K., Olesen, K. G., Jensen, F. V., Jensen, F.
Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell--for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a. genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components—nodes and arcs—as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an example of a real world application, MUN1N an expert system for electromyography is discussed.IJCAI-89, Vol. 2, pp. 1080–1085
A model for reasoning about persistence and causation
Dean, Thomas | Kanazawa., Keiji
Computational Intelligence Jnl., 5(3):142-150, 1989
Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals
Hanson, Stephen Jose, Burr, David J.
It can be shown that neural-like networks containing a single hidden layer of nonlinear activation units can learn to do a piece-wise linear partitioning of a feature space [2]. One result of such a partitioning is a complex gradient surface on which decisions about new input stimuli will be made. The generalization, categorization and clustering propenies of the network are therefore detennined by this mapping of input stimuli to this gradient swface in the output space. This gradient swface is a function of the conditional probability distributions of the output vectors given the input feature vectors as well as a function of the error relating the teacher signal and output.
Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals
Hanson, Stephen Jose, Burr, David J.
It can be shown that neural-like networks containing a single hidden layer of nonlinear activation units can learn to do a piece-wise linear partitioning of a feature space [2]. One result of such a partitioning is a complex gradient surface on which decisions about new input stimuli will be made. The generalization, categorization and clustering propenies of the network are therefore detennined by this mapping of input stimuli to this gradient swface in the output space. This gradient swface is a function of the conditional probability distributions of the output vectors given the input feature vectors as well as a function of the error relating the teacher signal and output.