Results 21 to 30 of about 353,766 (277)
Comparative Population Studies, 2021 This paper reviews empirical studies that have examined the causal determinants of fertility behaviour. In particular, we compare the approaches adopted in the different disciplines to improve our understanding of how birth dynamics are influenced by ...
Michaela Kreyenfeld
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Dynamic Causal Modelling of Hierarchical Planning
NeuroImage, 2022 Hierarchical planning (HP) is a strategy that optimizes the planning by storing the steps towards the goal (lower-level planning) into subgoals (higher-level planning).
Qunjun Liang +4 more
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The integrated use of enterprise and system dynamics modelling techniques in support of business decisions [PDF]
, 2012 Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements.
Agyapong-Kodua, Kwabena +2 more
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Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review [PDF]
International Journal of Environmental Research and Public Health, 2019 Nerea Almeda +2 more
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NeuroImage, 2003 In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters ...
K J, Friston, L, Harrison, W, Penny
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A categorical semantics for causal structure [PDF]
, 2019 We present a categorical construction for modelling causal structures within a general class of process theories that include the theory of classical probabilistic processes as well as quantum theory. Unlike prior constructions within categorical quantum
Kissinger, Aleks, Uijlen, Sander
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Imprecise Bayesian Networks as Causal Models
Information, 2018 This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for
David Kinney
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Applied Artificial Intelligence, 1995 Based on the causal ordering theory, the notion of conditional causality has been developed. Conditional causality allows for the development of conditional causal models, in which the world can be modeled as a set of variables. One variable can influence another by a direct link between the two.
Brée, D.S. +4 more
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Multisensory causal inference in the brain [PDF]
, 2015 At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from ...
A Pouget +31 more
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