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Regression modeling, time-series modeling, pattern recognition modeling and stochastic process modeling are tested to model the expected occupancy status and their performances are compared in terms of the degree of statistical approximation to actual occupancy.
In addition, comparison is made using two different modelings, random variable modeling and stochastic field modeling for the stochastic ground media.
The modeling method can be further divided into determinable modeling and stochastic modeling and is used to nicely reflect internal changes of geological body.
The results show that the personalized occupancy profiles acquired through time-series modeling, pattern recognition modeling and stochastic process modeling outperform the fixed design profiles and observation-based profiles currently used in building energy simulations.
Kinetic modeling (and stochastic modeling) requires detailed kinetic parameters and initial concentrations for each component in the network, which is usually not available for all the components in the network.
Hence, by mathematical modeling and stochastic
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During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.
Simulation concepts such as the static modeling, dynamic modeling, discrete event simulation, and stochastic modeling ("Monte Carlo") are presented.
In the mixed effects modeling approach, structural and stochastic parameters are simultaneously estimated by fitting the model to the data.
In this manuscript, we have tried to point out some of the issues that arise in interpreting the results of modeling and simulating discrete stochastic systems.
Presented numerical results of deterministic and stochastic modeling clearly reflect the advantage of the proposed design.
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