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Bayesian network is used to represent and predict an uncertain domain in a graphical structure [49].
Bayesian Belief Networks (also called Bayes nets or causal networks) are increasingly being used in natural hazards work as a method for reasoning about causal influences under conditions of observational uncertainty and for modelling uncertain domain states and conditions.
Here we present the results of an exploration into the complex and uncertain domain of urban governance assessments, focussing on sustainable water supplies for the benefit of the urban poor.
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Therefore, the robust stability is performed for generally bounded and uncertain domains.
This paper contributes a novel approach to planning in uncertain domains.
The optimization was preceded by sensitivity analysis to establish the design and uncertain domains.
If computers can drive cars and deal with all the knowledge in jeopardy, surely their next to next to next…generation can do diagnosis, treatment and teaching in these far less uncertain domains and with a lot more data.
Our paper covers theoretical results in knowledge validation for Bayesian Knowledge Bases (BKBs), a probabilistic model extended from Bayesian Networks for representing knowledge in uncertain domains.
By fully considering the property of transition rates and the characteristic of uncertain domains, a more tailored technique instead of the traditional Young inequality is proposed to bind the uncertain terms.
As both controller architecture and gain-scheduling structure are defined a priori, the scheduled gains can be tuned by the newly available MATLAB-based tool systune to minimize H∞ constraints related to performance requirements over both operating and uncertain domains.
By invoking the properties of the transition probability matrix, together with the convexification of uncertain domains, a new H∞ performance analysis criterion for the filtering error system is firstly derived.
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