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File - Assessment FOR Learning
File - Assessment FOR Learning

Chapter 17 notes - Phoenix Union High School District
Chapter 17 notes - Phoenix Union High School District

... Geometric models are completely specified by one parameter, p, the probability of success. Geometric probability model for Bernoulli trials: Geom(p) p = probability of success q = 1 – p = probability of failure x = # of trials until the first success occurs ...
Paper-I:(MCA-101): Introduction to Information Technology:
Paper-I:(MCA-101): Introduction to Information Technology:

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Brownie, C., King, L. D., and Dube, T. J. (2004Longitudinal and Spatial Analyses Applied to Corn Yield Data from a Long-Term Rotation Trial,"
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A novel multilayer neural network model for TOA
A novel multilayer neural network model for TOA

... weighted least squares (WLS) algorithm [11] is introduced which can tolerate unequally sized noises. However, since linearization of the TOA localization model is done under the assumption that the measurement noise is sufficiently small, the performance of LS and WLS declines considerably as the mea ...
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The Variance of the Estimator

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Robots mimic centipede`s surprising unstable advantage

... "Our group developed a mathematical model of centipedes and found that the straight walk becomes unstable and body undulations appear through a supercritical Hopf bifurcation by changing the locomotion speed and body axis flexibility," continues Aoi, referring to a mathematical description of the wa ...
A Discussion: Random Thoughts and Risky Propositions
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... –A “substance” causes what “conditions”? –A “condition” is caused by what “substances”? –Knowing “If” and “how much” may both be critical. –Which questions should be addressed? SAMSI 9/16-19/2007 Workshop (RTP, NC) ...
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Identification of unmeasured variables in the set of model constraints

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Application of Artificial Neural Network in Fluid Mechanics Teaching Evaluation System

... global learning rates are adjusted according to the error rates to improve the learning rate. They are fundamentally different from the traditional adaptive methods which include an additional momentum parameter for automatically improving the original gradient descent. In this study, we adopted a t ...
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Computer simulation



A computer simulation is a simulation, run on a single computer, or a network of computers, to reproduce behavior of a system. The simulation uses an abstract model (a computer model, or a computational model) to simulate the system. Computer simulations have become a useful part of mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry and biology, human systems in economics, psychology, social science, and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.Computer simulations vary from computer programs that run a few minutes to network-based groups of computers running for hours to ongoing simulations that run for days. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using traditional paper-and-pencil mathematical modeling. Over 10 years ago, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization ProgramOther examples include a 1-billion-atom model of material deformation; a 2.64-million-atom model of the complex maker of protein in all organisms, a ribosome, in 2005;a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification.
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