Results 11 to 20 of about 6,659,086 (346)
Stochastic Kriging for Simulation Metamodeling [PDF]
Operations Research, 2008 We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables, or uncontrollable ...
Ankenman, Bruce E. +2 more
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Spike Sorting by Stochastic Simulation [PDF]
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011 The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials.
Ge, Di +3 more
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Stochastic simulations of the repressilator circuit [PDF]
Physical Review E, 2007 Accepted to ...
Loinger, Adiel, Biham, Ofer
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Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme
Journal of Advances in Modeling Earth Systems, 2023 A new spectral convection scheme, the stochastic unified convection scheme (stochastic UNICON), is implemented in a general circulation model. The global climate simulation using stochastic UNICON is evaluated and compared with UNICON, focusing on the ...
Jihoon Shin, Jong‐Jin Baik
doaj +1 more source
StochPy: a comprehensive, user-friendly tool for simulating stochastic biological processes. [PDF]
PLoS ONE, 2013 Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically ...
Timo R Maarleveld +2 more
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Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory
Entropy, 2020 Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training ...
Sergey Oladyshkin +3 more
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Renewable Energy, 2020 As installed wind generation capacities increase, there is a need to model variability in wind generation in detail to analyse its impacts on power systems.
M. Koivisto +4 more
semanticscholar +1 more source
Intrinsic Simulations between Stochastic Cellular Automata [PDF]
Electronic Proceedings in Theoretical Computer Science, 2012 The paper proposes a simple formalism for dealing with deterministic, non-deterministic and stochastic cellular automata in a unifying and composable manner.
Pablo Arrighi +2 more
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A multiobjective stochastic simulation optimization algorithm
European Journal of Operational Research, 2020 The use of kriging metamodels in simulation optimization has become increasingly popular during recent years. The majority of the algorithms so far uses the ordinary (deterministic) kriging approach for constructing the metamodel, assuming that solutions
Sebastian Rojas-Gonzalez +2 more
semanticscholar +1 more source
Journal of Biological Dynamics, 2023 A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared ...
Raimund Bürger +3 more
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