scholarly journals Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset

Data in Brief ◽  
2022 ◽  
pp. 107783
Author(s):  
Daniel Canton Enriquez ◽  
Jose A. Niembro-Ceceña ◽  
Martin Muñoz Mandujano ◽  
Daniel Alarcon ◽  
Jorge Arcadia Guerrero ◽  
...  
Keyword(s):  
Probabilistic Models ◽  
Extreme Values

scholarly journals Probabilistic models for estimation extreme values of watershed flow rates

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Leandro Campos Pinto ◽  
Pedro Terra Lima ◽  
João José Da Silva Junior
Keyword(s):  
Probabilistic Models ◽  
Extreme Values ◽  
Flow Rates

Non Parametric Probabilistic Approach of Ice Load Peaks on Ship Hulls

2012 ◽  
Author(s):  
Abdillah Suyuthi ◽  
Bernt J. Leira ◽  
Kaj Riska
Keyword(s):  
Probabilistic Models ◽  
Extreme Values ◽  
Probabilistic Approach ◽  
Initial Distribution ◽  
Complex Phenomenon ◽  
Short Term ◽  
Ship Hulls ◽  
Ice Load ◽  
Non Parametric

Due to its complex phenomenon, ice-induced load process could not be physically possible to be modeled by a specific well established probabilistic model. Therefore, a non parametric approximate probabilistic approach should be performed based on the available data. This paper describes the procedure and also the extrapolation to get the short term and long term extreme values. Comparison with the classical approach, where the initial distribution of ice-induced load is assumed to be one of the well established probabilistic models, was made. The comparisons discussed in this paper were explored by using the same available data of full scale measurement on board a coastal guard vessel KV Svalbard during the winter 2007. There was a tendency that the non parametric approach produces more conservative results.

On large values of L(σ,χ)

2018 ◽  
Vol 70 (3) ◽  
pp. 831-848 ◽  
Author(s):  
Christoph Aistleitner ◽  
Kamalakshya Mahatab ◽  
Marc Munsch ◽  
Alexandre Peyrot
Keyword(s):  
Zeta Function ◽  
Riemann Zeta Function ◽  
Probabilistic Models ◽  
Extreme Values ◽  
Resonance Method ◽  
Critical Strip ◽  
Principal Character ◽  
Large Order ◽  
The Riemann Zeta Function ◽  
Riemann Zeta

Abstract In recent years, a variant of the resonance method was developed which allowed to obtain improved Ω-results for the Riemann zeta function along vertical lines in the critical strip. In the present paper, we show how this method can be adapted to prove the existence of large values of |L(σ,χ)| in the range σ∈(1/2,1], and to estimate the proportion of characters for which |L(σ,χ)| is of such a large order. More precisely, for every fixed σ∈(1/2,1), we show that for all sufficiently large q, there is a non-principal character χ(modq) such that log|L(σ,χ)|≥C(σ)(logq)1−σ(loglogq)−σ. In the case σ=1, we show that there is a non-principal character χ(modq) for which |L(1,χ)|≥eγ(log2q+log3q−C). In both cases, our results essentially match the prediction for the actual order of such extreme values, based on probabilistic models.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

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