Works matching DE "STATISTICAL ensembles"
Results: 434
ENSEMBLE CONTROLLABILITY BY LIE ALGEBRAIC METHODS.
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- ESAIM: Control, Optimisation & Calculus of Variations, 2016, v. 22, n. 4, p. 921, doi. 10.1051/cocv/2016029
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Mollack: a web server for the automated creation of conformational ensembles for intrinsically disordered proteins.
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- Bioinformatics, 2016, v. 32, n. 16, p. 2545, doi. 10.1093/bioinformatics/btw200
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EnsembleGASVR: a novel ensemble method for classifying missense single nucleotide polymorphisms.
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- Bioinformatics, 2014, v. 30, n. 16, p. 2324, doi. 10.1093/bioinformatics/btu297
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Inductive ensemble clustering using kernel support matching.
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- Electronics Letters (Wiley-Blackwell), 2017, v. 53, n. 25, p. 1625, doi. 10.1049/el.2017.2159
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A result on precise asymptotics for largest eigenvalues of β ensembles.
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- Journal of Inequalities & Applications, 2014, v. 2014, p. 1, doi. 10.1186/1029-242X-2014-408
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Ensemble properties of high-frequency data and intraday trading rules.
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- Quantitative Finance, 2015, v. 15, n. 2, p. 231, doi. 10.1080/14697688.2013.867454
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Logistic‐growth models measuring density feedback are sensitive to population declines, but not fluctuating carrying capacity.
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- Ecology & Evolution (20457758), 2023, v. 13, n. 4, p. 1, doi. 10.1002/ece3.10010
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Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy.
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- Shock & Vibration, 2016, p. 1, doi. 10.1155/2016/4805383
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Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-43415-0
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Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-43415-0
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Effects of Boundary Conditions on Microstructure-Sensitive Fatigue Crystal Plasticity Analysis.
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- Integrating Materials & Manufacturing Innovation, 2021, v. 10, n. 3, p. 393, doi. 10.1007/s40192-021-00219-2
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Potential Contour Ensembles.
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- Schedae Informaticae, 2015, v. 24, p. 113, doi. 10.4467/20838476SI.15.011.3033
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Moderate deviations for extreme eigenvalues of beta-Laguerre ensembles.
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- Random Matrices: Theory & Application, 2020, v. 09, n. 02, p. N.PAG, doi. 10.1142/S2010326320500033
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Transport maps for -matrix models in the multi-cut regime.
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- Random Matrices: Theory & Application, 2018, v. 7, n. 1, p. -1, doi. 10.1142/S2010326317500137
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Predicting Air Traffic Congestion under Uncertain Adverse Weather.
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- Aerospace (MDPI Publishing), 2024, v. 11, n. 3, p. 240, doi. 10.3390/aerospace11030240
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A practical model for ensemble estimation of QoS and QoE in VoIP services via fuzzy inference systems and fuzzy evidence theory.
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- Telecommunication Systems, 2016, v. 61, n. 4, p. 861, doi. 10.1007/s11235-015-0041-6
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Multi-objective ensemble generation.
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- WIREs: Data Mining & Knowledge Discovery, 2015, v. 5, n. 5, p. 234, doi. 10.1002/widm.1158
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Surface‐supported cluster catalysis: Ensembles of metastable states run the show.
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- WIREs: Computational Molecular Science, 2019, v. 9, n. 6, p. N.PAG, doi. 10.1002/wcms.1420
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Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China.
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- Atmosphere, 2023, v. 14, n. 10, p. 1474, doi. 10.3390/atmos14101474
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Simulation Study of the Applicability of the "Slice" Approach to Assessing the Water Content in Clouds from the Radar Return Signal.
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- Atmosphere, 2023, v. 14, n. 1, p. 42, doi. 10.3390/atmos14010042
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Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing.
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- Atmosphere, 2021, v. 12, n. 8, p. 966, doi. 10.3390/atmos12080966
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Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework.
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- Atmosphere, 2021, v. 12, n. 1, p. 97, doi. 10.3390/atmos12010097
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Flood susceptibility assessment and mapping in a monsoon-dominated tropical river basin using GIS-based data-driven bivariate and multivariate statistical models and their ensemble techniques.
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- Environmental Earth Sciences, 2023, v. 82, n. 1, p. 1, doi. 10.1007/s12665-022-10696-z
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ST-TransNet: A Spatiotemporal Transformer Network for Uncertainty Estimation from a Single Deterministic Precipitation Forecast.
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- Monthly Weather Review, 2024, v. 152, n. 5, p. 1163, doi. 10.1175/MWR-D-23-0097.1
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Tropical Cyclone Track Sensitivity in Deformation Steering Flow.
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- Monthly Weather Review, 2018, v. 146, n. 10, p. 3183, doi. 10.1175/MWR-D-18-0153.1
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The Stability of Incremental Analysis Update.
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- Monthly Weather Review, 2018, v. 146, n. 10, p. 3259, doi. 10.1175/MWR-D-18-0117.1
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Ensemble-Based Atmospheric Reanalysis Using a Global Coupled Atmosphere–Ocean GCM.
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- Monthly Weather Review, 2018, v. 146, n. 10, p. 3311, doi. 10.1175/MWR-D-17-0361.1
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Use of Ensemble Forecasts to Investigate Synoptic Influences on the Structural Evolution and Predictability of Hurricane Alex (2016) in the Midlatitudes.
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- Monthly Weather Review, 2018, v. 146, n. 10, p. 3143, doi. 10.1175/MWR-D-18-0015.1
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Can the Direct Effect of Aerosols Improve Subseasonal Predictability?.
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- Monthly Weather Review, 2018, v. 146, n. 10, p. 3481, doi. 10.1175/MWR-D-17-0282.1
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Improving Particle Filter Performance by Smoothing Observations.
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- Monthly Weather Review, 2018, v. 146, n. 8, p. 2433, doi. 10.1175/MWR-D-17-0349.1
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High-Altitude (0–100 km) Global Atmospheric Reanalysis System: Description and Application to the 2014 Austral Winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE).
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- Monthly Weather Review, 2018, v. 146, n. 8, p. 2639, doi. 10.1175/MWR-D-17-0386.1
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Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread.
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- Monthly Weather Review, 2018, v. 146, n. 8, p. 2361, doi. 10.1175/MWR-D-17-0374.1
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Using Evolutionary Programming to Add Deterministic and Probabilistic Skill to Spatial Model Forecasts.
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- Monthly Weather Review, 2018, v. 146, n. 8, p. 2525, doi. 10.1175/MWR-D-17-0272.1
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A Stochastic Perturbed Parameterization Tendency Scheme for Diffusion (SPPTD) and Its Application to an Idealized Supercell Simulation.
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- Monthly Weather Review, 2017, v. 145, n. 6, p. 2119, doi. 10.1175/MWR-D-16-0307.1
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On Cost Functions in the Hybrid Variational-Ensemble Method.
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- Monthly Weather Review, 2017, v. 145, n. 6, p. 2071, doi. 10.1175/MWR-D-16-0325.1
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Automatic Software Categorization Using Ensemble Methods and Bytecode Analysis.
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- International Journal of Software Engineering & Knowledge Engineering, 2017, v. 27, n. 7, p. 1129, doi. 10.1142/S0218194017500425
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Guest Editor's Introduction.
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- International Journal of Software Engineering & Knowledge Engineering, 2014, v. 24, n. 9, p. 1227, doi. 10.1142/S0218194014020045
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The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction.
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- International Journal of Software Engineering & Knowledge Engineering, 2014, v. 24, n. 9, p. 1229, doi. 10.1142/S0218194014400105
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Ensemble Simulations and Pullback Attractors of a Periodically Forced Double-Gyre System.
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- Journal of Physical Oceanography, 2014, v. 44, n. 12, p. 3245, doi. 10.1175/JPO-D-14-0117.1
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Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case.
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- Advances in Atmospheric Sciences, 2024, v. 41, n. 2, p. 263, doi. 10.1007/s00376-023-2340-2
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CMIP5 simulated change in the intensity of the Hadley and Walker circulations from the perspective of velocity potential.
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- Advances in Atmospheric Sciences, 2016, v. 33, n. 7, p. 808, doi. 10.1007/s00376-016-5216-x
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Using analysis state to construct a forecast error covariance matrix in ensemble Kalman filter assimilation.
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- Advances in Atmospheric Sciences, 2013, v. 30, n. 5, p. 1303, doi. 10.1007/s00376-012-2133-5
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Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA.
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- Advances in Atmospheric Sciences, 2013, v. 30, n. 5, p. 1287, doi. 10.1007/s00376-012-2083-y
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SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine.
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- Computational Intelligence & Neuroscience, 2018, p. 1, doi. 10.1155/2018/4058403
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Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection.
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- Computational Intelligence & Neuroscience, 2017, p. 1, doi. 10.1155/2017/2658707
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1052. Ensemble learning-based intelligent fault diagnosis method using feature partitioning.
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- Journal of Vibroengineering, 2013, v. 15, n. 3, p. 1378
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Uncovering the shortcomings of a weather typing method.
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- Hydrology & Earth System Sciences, 2020, v. 24, n. 5, p. 2671, doi. 10.5194/hess-24-2671-2020
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Identification of hydrological model parameter variation using ensemble Kalman filter.
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- Hydrology & Earth System Sciences, 2016, v. 20, n. 12, p. 4949, doi. 10.5194/hess-20-4949-2016
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Digital Ensemble: The ENa CT design-based research framework for technology-enhanced embodied assessment in English education.
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- English in Education, 2017, v. 51, n. 1, p. 76, doi. 10.1111/eie.12136
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A second-order ensemble method based on a blended backward differentiation formula timestepping scheme for time-dependent Navier-Stokes equations.
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- Numerical Methods for Partial Differential Equations, 2017, v. 33, n. 1, p. 34, doi. 10.1002/num.22070
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