Results 1 to 10 of about 86,090 (144)
Probabilistic Modeling and Estimation of Flow Rate of Sewage Treatment Plant Using Monte Carlo Hybrid Method [PDF]
آب و فاضلاب, 2021 One of the most important results of hydraulic and hydrological modeling of the urban drainage network is hydrograph estimation. Annual journals on hydraulic and hydrological problems, especially in developing countries, are full of missing data ...
Jamshid Piri +3 more
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Sigma-Lognormal Modeling of Speech [PDF]
Cognitive Computation, 2021 AbstractHuman movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are ...
Cristina Carmona-Duarte +4 more
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Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators
Frontiers in Human Neuroscience, 2021 Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades.
Henrik Lindén, Rune W. Berg
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Model-Driven Analysis of ECG Using Reinforcement Learning
Bioengineering, 2023 Modeling is essential to better understand the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG).
Christian O’Reilly +3 more
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Observational and Critical State Physics Descriptions of Long-Range Flow Structures
Geosciences, 2020 Using Fracture Seismic methods to map fluid-conducting fracture zones makes it important to understand fracture connectivity over distances greater 10−20 m in the Earth’s upper crust.
Peter E. Malin +3 more
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A Framework for Analysis and Prediction of Operational Risk Stress
Mathematical and Computational Applications, 2021 A model for financial stress testing and stability analysis is presented. Given operational risk loss data within a time window, short-term projections are made using Loess fits to sequences of lognormal parameters.
Peter Mitic
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On the three-spliced Exponential-Lognormal-Pareto distribution
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2022 To model statistical data coming from two di erent distributions, Cooray and Ananda [1] introduced a composite (two-spliced) Lognormal-Pareto model, that was further extended by Scollnik [9] and fitted to insurance data.
Bâcă Adrian, Vernic Raluca
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On the Generalized Lognormal Distribution [PDF]
Journal of Probability and Statistics, 2013 This paper introduces, investigates, and discusses the -order generalized lognormal distribution (-GLD). Under certain values of the extra shape parameter , the usual lognormal, log-Laplace, and log-uniform distribution, are obtained, as well as the degenerate Dirac distribution. The shape of all the members of the -GLD family is extensively discussed.
Thomas L. Toulias, Christos P. Kitsos
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BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding
Entropy, 2023 Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques.
Lorenzo Dall’Olio +4 more
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On the lognormality of rain rate [PDF]
Proceedings of the National Academy of Sciences, 1987 A stochastic regression model is used in modeling rain rate. Under some conditions on the model parameters, it is shown that rain rate is asymptotically lognormal. An application of the model to the GATE (global atmospheric research program, Atlantic tropical experiment) data shows a remarkable agreement between the assumed and estimated model ...
Kedem, Benjamin, Chiu, Long S.
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