Additive noise model structure learning based on rank correlation

2021 ◽  
Author(s):  
Jing Yang ◽  
Gaojin Fan ◽  
Kai Xie ◽  
Qiqi Chen ◽  
Aiguo Wang
Keyword(s):  
Additive Noise ◽  
Structure Learning ◽  
Rank Correlation ◽  
Noise Model ◽  
Model Structure

scholarly journals DENOISING AND ENHANCEMENT OF MAMMOGRAPHIC IMAGES UNDER THE ASSUMPTION OF HETEROSCEDASTIC ADDITIVE NOISE BY AN OPTIMAL SUBBAND THRESHOLDING

2010 ◽  
Vol 08 (05) ◽  
pp. 713-741 ◽  
Author(s):  
ARIANNA MENCATTINI ◽  
GIULIA RABOTTINO ◽  
MARCELLO SALMERI ◽  
ROBERTO LOJACONO ◽  
BERARDINO SCIUNZI
Keyword(s):  
Wavelet Transforms ◽  
Additive Noise ◽  
Subjective Evaluation ◽  
Visual Assessment ◽  
Noise Model ◽  
Zero Approximation ◽  
Low Contrast ◽  
Mammographic Images ◽  
Dyadic Wavelet ◽  
Expert Radiologist

Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation.

scholarly journals Analyzing the Effects of the Dwelling Characteristics on Space Heating Costs with the Rank Correlation Bayesian Network Model

2021 ◽  
Author(s):  
Volkan Sevinç
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Model ◽  
Building Materials ◽  
Structure Learning ◽  
Rank Correlation ◽  
Space Heating ◽  
Bayesian Network Model ◽  
Heating Systems ◽  
External Wall

Abstract Energy is one of the main concerns of humanity because energy resources are limited and costly. In order to reduce the costs and to use the energy for space heating effectively, new building materials, techniques and insulations facilities are being developed. Therefore, it is important to know which factors affect the space heating costs. This study aims to introduce the novel Rank Correlation Bayesian Network model and its application in analyzing the effects of dwelling characteristics on the space heating costs. The results show that the constructed Rank Correlation Bayesian Network model performed better than the Bayesian networks models estimated by Bayesian search, PC and Greedy Thick Thinning algorithms, which are kinds of structure learning algorithms having different kinds of estimation mechanisms to build Bayesian networks. The constructed Rank Correlation Bayesian Network model shows that the space heating costs of the dwellings are mostly affected by the heating systems used. Coal stoves, air conditioners and electric stoves appear to be the costliest heating systems. The second most important factor appears to be the existence of external wall insulation. The lack of external wall insulation almost doubles the space heating costs. The third most important factor is the building age. Dwellings on the ground floors and the first floors appear to pay the highest space heating costs. Therefore, dwellings on these floors need to be more effectively insulated. As the size of the dwelling increases the heating cost increases too. Another result is that facing directions and floor levels of the dwellings have the least effects on their space heating.

scholarly journals On the Intersection Property of Conditional Independence and its Application to Causal Discovery

2015 ◽  
Vol 3 (1) ◽  
pp. 97-108
Author(s):  
Jonas Peters
Keyword(s):  
Conditional Independence ◽  
Joint Distribution ◽  
Additive Noise ◽  
Sufficient Conditions ◽  
Intersection Property ◽  
Noise Model ◽  
Statistical Independence ◽  
Necessary And Sufficient ◽  
Weaker Conditions ◽  
Graphical Structure

AbstractThis work investigates the intersection property of conditional independence. It states that for random variables $$A,B,C$$ and X we have that $$X \bot \bot A{\kern 1pt} {\kern 1pt} |{\kern 1pt} {\kern 1pt} B,C$$ and $$X\, \bot \bot\, B{\kern 1pt} {\kern 1pt} |{\kern 1pt} {\kern 1pt} A,C$$ implies $$X\, \bot \bot\, (A,B){\kern 1pt} {\kern 1pt} |{\kern 1pt} {\kern 1pt} C$$. Here, “$$ \bot \bot $$” stands for statistical independence. Under the assumption that the joint distribution has a density that is continuous in $$A,B$$ and C, we provide necessary and sufficient conditions under which the intersection property holds. The result has direct applications to causal inference: it leads to strictly weaker conditions under which the graphical structure becomes identifiable from the joint distribution of an additive noise model.

Stochastic models with multiplicative noise for economic inequality and mobility

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Maria Letizia Bertotti ◽  
Amit K Chattopadhyay ◽  
Giovanni Modanese
Keyword(s):  
Stochastic Models ◽  
Gini Index ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Noise Model ◽  
Total Income ◽  
Mobility Index ◽  
Model Dynamics ◽  
Economic Exchanges ◽  
Dynamical Parameters

Abstract In this article, we discuss a dynamical stochastic model that represents the time evolution of income distribution of a population, where the dynamics develops from an interplay of multiple economic exchanges in the presence of multiplicative noise. The model remit stretches beyond the conventional framework of a Langevin-type kinetic equation in that our model dynamics is self-consistently constrained by dynamical conservation laws emerging from population and wealth conservation. This model is numerically solved and analysed to evaluate the inequality of income in correlation to other relevant dynamical parameters like the mobility M and the total income μ. Inequality is quantified by the Gini index G. In particular, correlations between any two of the mobility index M and/or the total income μ with the Gini index G are investigated and compared with the analogous quantities resulting from an additive noise model.

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