Gary White Explains How to Plot Basic Error Bars in Matplotlib

2020 ◽  
Keyword(s):  
Author(s):  
Ondrej Gutten ◽  
Petr Jurečka ◽  
Zahra Aliakbar Tehrani ◽  
Miloš Buděšínský ◽  
Jan Řezáč ◽  
...  

Computational “error bars” for modelling cyclic dinucleotides – NMR experiment vs. quantum mechanics and molecular dynamics.


2020 ◽  
Vol 26 (3) ◽  
pp. 469-483
Author(s):  
Nicholas W. M. Ritchie

AbstractThis is the first in a series of articles which present a new framework for computing the standard uncertainty in electron excited X-ray microanalysis measurements. This article will discuss the framework and apply it to a handful of simple, but useful, subcomponents of the larger problem. Subsequent articles will handle more complex aspects of the measurement model. The result will be a framework in which sophisticated and practical models of the uncertainty for real-world measurements. It will include many long overlooked contributions like surface roughness and coating thickness. The result provides more than just error bars for our measurements. It also provides a framework for measurement optimization and, ultimately, the development of an expert system to guide both the novice and expert to design more effective measurement protocols.


Author(s):  
Henry Scown ◽  
Megan Bartlett ◽  
Jason S. McCarley
Keyword(s):  

Author(s):  
P Santos-Sanz ◽  
J L Ortiz ◽  
B Sicardy ◽  
G Benedetti-Rossi ◽  
N Morales ◽  
...  

Abstract We predicted a stellar occultation of the bright star Gaia DR1 4332852996360346368 (UCAC4 385-75921) (mV= 14.0 mag) by the centaur 2002 GZ32 for 2017 May 20th. Our latest shadow path prediction was favourable to a large region in Europe. Observations were arranged in a broad region inside the nominal shadow path. Series of images were obtained with 29 telescopes throughout Europe and from six of them (five in Spain and one in Greece) we detected the occultation. This is the fourth centaur, besides Chariklo, Chiron and Bienor, for which a multi-chord stellar occultation is reported. By means of an elliptical fit to the occultation chords we obtained the limb of 2002 GZ32 during the occultation, resulting in an ellipse with axes of 305 ± 17 km × 146 ± 8 km. From this limb, thanks to a rotational light curve obtained shortly after the occultation, we derived the geometric albedo of 2002 GZ32 (pV = 0.043 ± 0.007) and a 3-D ellipsoidal shape with axes 366 km × 306 km × 120 km. This shape is not fully consistent with a homogeneous body in hydrostatic equilibrium for the known rotation period of 2002 GZ32. The size (albedo) obtained from the occultation is respectively smaller (greater) than that derived from the radiometric technique but compatible within error bars. No rings or debris around 2002 GZ32 were detected from the occultation, but narrow and thin rings cannot be discarded.


Author(s):  
Weitao Li ◽  
Liping Wang

Abstract Parallel manipulators have broad application prospects on hybrid machine tools. Kinematic error modelling and identification are two key processes to improve the accuracy of parallel manipulators. The traditional kinematic error modelling method adopts the partial differentiation of the ideal kinematic model. However, the partial differentiation method is pure mathematical calculation, which ignores physical meaning of error terms corresponding to each link. In the process of error identification, the Jacobian matrix obtained from the partial differentiation method is usually ill-conditioned, which leads to non-convergence of the identification process. In order to solve the above problems, this paper proposes a new kinematic error modelling method and an error identification model. Firstly, the basic error terms for single link are analyzed. Based on basic error terms, the kinematic error model is established by using the practical connection point of two adjacent links. Then, a new error identification model is derived from the kinematic error model. Finally, as a study case, a 3-DOF parallel tool head is used to verify the correctness of the proposed method. The numerical results show that the proposed method is effective and the accuracy of the 3-DOF parallel tool head improves significantly after compensation of error terms.


1998 ◽  
Vol 10 (3) ◽  
pp. 731-747 ◽  
Author(s):  
Volker Tresp ◽  
Reimar Hofmann

We derive solutions for the problem of missing and noisy data in nonlinear time-series prediction from a probabilistic point of view. We discuss different approximations to the solutions—in particular, approximations that require either stochastic simulation or the substitution of a single estimate for the missing data. We show experimentally that commonly used heuristics can lead to suboptimal solutions. We show how error bars for the predictions can be derived and how our results can be applied to K-step prediction. We verify our solutions using two chaotic time series and the sunspot data set. In particular, we show that for K-step prediction, stochastic simulation is superior to simply iterating the predictor.


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