The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models

2022 ◽  
pp. 120-140
Author(s):  
Benjamin Kap ◽  
Marharyta Aleksandrova ◽  
Thomas Engel
Keyword(s):  
Noise Level ◽  
Additive Noise ◽  
Causal Discovery ◽  
Noise Models

Nonequilibrium Phenomena in Globally Coupled Phase Oscillators: Noise-Induced Bifurcations, Clustering, and Switching

1997 ◽  
Vol 07 (04) ◽  
pp. 917-922
Author(s):  
Seon Hee Park ◽  
Seunghwan Kim ◽  
Seung Kee Han
Keyword(s):  
Phase Transition ◽  
Noise Intensity ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Critical Value ◽  
Phase Oscillators ◽  
Deterministic Systems ◽  
Nonequilibrium Phenomena ◽  
Noise Models ◽  
Coupled Phase Oscillators

The Nonequilibrium phenomena in a class of globally coupled phase oscillators systems with multiplicative noise are studied. It is shown that at the critical value of the noise intensity the systems undergo a phase transition and converge to clustered states. We also show that the time delay in the interaction between oscillators gives rise to the switching phenomena of clusters. These phenomena are noise-induced effects which cannot be seen in the deterministic systems or in the simple additive noise models.

scholarly journals Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

2018 ◽  
Vol 37 (75) ◽  
pp. 779-808 ◽  
Author(s):  
Alex Coad ◽  
Dominik Janzing ◽  
Paul Nightingale
Keyword(s):  
Causal Inference ◽  
Learning Community ◽  
Firm Growth ◽  
Additive Noise ◽  
Public Funding ◽  
Data Driven ◽  
Cross Sectional ◽  
Innovation Surveys ◽  
Noise Models ◽  
D Investment

This paper presents a new statistical toolkit by applying three techniques for data-driven causal inference from the machine learning community that are little-known among economists and innovation scholars: a conditional independence-based approach, additive noise models, and non-algorithmic inference by hand. We include three applications to CIS data to investigate public funding schemes for R&D investment, information sources for innovation, and innovation expenditures and firm growth. Preliminary results provide causal interpretations of some previously-observed correlations. Our statistical 'toolkit' could be a useful complement to existing techniques.

scholarly journals Validating Aircraft Noise Models

Proceedings ◽  
2020 ◽  
Vol 59 (1) ◽  
pp. 12
Author(s):  
Ran Giladi ◽  
Eliav Menachi
Keyword(s):  
Noise Level ◽  
Health Hazard ◽  
Aircraft Noise ◽  
Noise Model ◽  
Aviation Industry ◽  
Noise Abatement ◽  
Statistical Variation ◽  
Huge Impact ◽  
Aircraft Type ◽  
Noise Models

Aircraft noise, especially at takeoffs and landings, became a major environmental nuisance and a health hazard for the population around metropolitan airports. In the battle for a better quality of life, wellbeing, and health, aircraft noise models are essential for noise abatement, control, enforcement, evaluation, policy-making, and shaping the entire aviation industry. Aircraft noise models calculate noise and exposure levels based on aircraft types, engines and airframes, aircraft flight paths, environment factors, and more. Validating the aircraft noise model is a mandatory step towards the model credibility, especially when these models play such a key role with a huge impact on society, economy, and public health. Yet, no validation procedure was offered, and it turns out to be a challenging task. The actual, measured, aircraft noise level is known to be subject to statistical variation, even for the same aircraft type at the same situation and flight phase, executing the same flight procedure, with similar environmental factors and at the same place. This study tries to validate the FAA’s AEDT aircraft noise model, by trying to correlate the specific flight path of an aircraft with its measured noise level. The results show that the AEDT noise model underestimates the actual noise level, and four validation steps should be performed to correct or tune aircraft noise databases and flight profiles.

Seismic noise characteristics in Chinese mainland

2020 ◽  
Author(s):  
Fang Wang ◽  
Weitao Wang ◽  
Jianfeng Long ◽  
Leiyu Mu
Keyword(s):  
Noise Level ◽  
Seismic Noise ◽  
Vertical Component ◽  
Chinese Mainland ◽  
Low Noise ◽  
Noise Characteristics ◽  
Power Spectral ◽  
Short Period ◽  
Power Spectral Densities ◽  
Noise Models

<p>Using the three-component continuous waveform recordings of 880 broadband seismic stations in China Seismic Network from January 2014 to December 2015, we calculated power spectral densities and probability density functions over the entire period for each station，and  investigated the characteristics of seismic noise in Chinese mainland. The deep analysis on the vertical recordings  indicates that the spatial distribution of noise levels is characterized by obvious zoning for different period bands.  Densely populated areas have higher short-period noise level than sparsely populated ones, suggesting that short-period noise is related to the intensity distribution of human activities such as transportation and industry. Meanwhile，the short-period noise level near the basin is higher than the mountainous areas，which is probably caused by the amplification effect of the sedimentary layer. The microseism energy  gradually decreases from the southeastern coastal lines to the inland regions. Furthermore, horizontal-component noise level  showed a striking constrast with the vertical component at microseismic and long-period bands. In consideration of  the zoning chracteristics and the need of seismic observations, high and low noise models were  acquired for each network , which were proved to be a more effective tool to identify locally abnormal signals including earthquake, instrumental error and various distrubance compared with the global new high and low model. </p>

scholarly journals Coupling Reduces Noise: Applying Dynamical Coupling to Reduce Local White Additive Noise

2015 ◽  
Vol 25 (03) ◽  
pp. 1550040 ◽  
Author(s):  
Behnam Kia ◽  
Sarvenaz Kia ◽  
John F. Lindner ◽  
Sudeshna Sinha ◽  
William L. Ditto
Keyword(s):  
Theoretical Model ◽  
Dynamical Systems ◽  
Noise Level ◽  
Additive Noise ◽  
Nonlinear Dynamical Systems ◽  
Coupled Map Lattices ◽  
Nonlinear Dynamical ◽  
Model Predictions ◽  
Dynamical Coupling ◽  
Averaging Filter

We demonstrate how coupling nonlinear dynamical systems can reduce the effects of noise. For simplicity we investigate noisy coupled map lattices and assume noise is white and additive. Noise from different lattice nodes can diffuse across the lattice and lower the noise level of individual nodes. We develop a theoretical model that explains this observed noise evolution and show how the coupled dynamics can naturally function as an averaging filter. Our numerical simulations are in excellent agreement with the model predictions.

scholarly journals From Annotator Agreement to Noise Models

2009 ◽  
Vol 35 (4) ◽  
pp. 495-503 ◽  
Author(s):  
Beata Beigman Klebanov ◽  
Eyal Beigman
Keyword(s):  
Gold Standard ◽  
Noise Level ◽  
Reliable Data ◽  
Mathematical Framework ◽  
High Confidence ◽  
Data Set ◽  
Noise Models

This article discusses the transition from annotated data to a gold standard, that is, a subset that is sufficiently noise-free with high confidence. Unless appropriately reinterpreted, agreement coefficients do not indicate the quality of the data set as a benchmarking resource: High overall agreement is neither sufficient nor necessary to distill some amount of highly reliable data from the annotated material. A mathematical framework is developed that allows estimation of the noise level of the agreed subset of annotated data, which helps promote cautious benchmarking.

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