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Reliability and Statistics in Transportation and Communication

Posted By: AvaxGenius
Date: 2 Feb 2024 04:14:41
Reliability and Statistics in Transportation and Communication

Reliability and Statistics in Transportation and Communication: Selected Papers from the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication: Digital Twins - From Development to Application, RelStat-2023, October 19-21, 2023, Riga, Latvia by Igor Kabashkin, Irina Yatskiv, Olegas Prentkovskis
English | PDF (True) | 2024 | 621 Pages | ISBN : 3031535979 | 32.5 MB

This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most relevant findings discussed at the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2023), which took place as a hybrid event on October 19 – 21, 2023, in/from Riga, Latvia. It spans a broad spectrum of advanced theories and methods, giving a special emphasis to the digitalization of transport systems, as well as smart, artificial intelligence, and digital twins applications.
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The Nature of Statistical Learning Theory

Posted By: AvaxGenius
Date: 8 Jan 2024 02:06:41
The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory by Vladimir N. Vapnik
English | PDF | 2000 | 324 Pages | ISBN : 0387987800 | 22.6 MB

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size.
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Advances in Data Analysis (Repost)

Posted By: AvaxGenius
Date: 24 Apr 2022 05:47:33
Advances in Data Analysis (Repost)

Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks by Christos H. Skiadas
English | PDF | 2010 | 368 Pages | ISBN : 0817647988 | 5.8 MB

An outgrowth of the 12th International Conference on Applied Stochastic Models and Data Analysis, this book is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Emphasized throughout the volume are new methods with the potential for solving real-world problems in various areas.
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Computational Methods in Stochastic Dynamics: Volume 2 (Repost)

Posted By: AvaxGenius
Date: 2 Apr 2022 06:57:34
Computational Methods in Stochastic Dynamics: Volume 2 (Repost)

Computational Methods in Stochastic Dynamics: Volume 2 by Manolis Papadrakakis
English | PDF | 2013 | 362 Pages | ISBN : 9400751338 | 12.9 MB

The considerable influence of inherent uncertainties on structural behavior has led the engineering community to recognize the importance of a stochastic approach to structural problems. Issues related to uncertainty quantification and its influence on the reliability of the computational models are continuously gaining in significance. In particular, the problems of dynamic response analysis and reliability assessment of structures with uncertain system and excitation parameters have been the subject of continuous research over the last two decades as a result of the increasing availability of powerful computing resources and technology.
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Statistics for Mathematicians: A Rigorous First Course

Posted By: AvaxGenius
Date: 2 Apr 2022 04:42:35
Statistics for Mathematicians: A Rigorous First Course

Statistics for Mathematicians: A Rigorous First Course By Victor M. Panaretos
English | PDF | 2016 | 177 Pages | ISBN : 3319283391 | 2.6 MB

This textbook provides a coherent introduction to the main concepts and methods of one-parameter statistical inference. Intended for students of Mathematics taking their first course in Statistics, the focus is on Statistics for Mathematicians rather than on Mathematical Statistics.
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Statistical Image Processing Techniques for Noisy Images: An Application-Oriented Approach (Repost)

Posted By: AvaxGenius
Date: 20 Feb 2022 22:46:38
Statistical Image Processing Techniques for Noisy Images: An Application-Oriented Approach (Repost)

Statistical Image Processing Techniques for Noisy Images: An Application-Oriented Approach by François Goudail
English | PDF | 2004 | 261 Pages | ISBN : 030647865X | 27 MB

Statistical Processing Techniques for Noisy Images presents a statistical framework to design algorithms for target detection, tracking, segmentation and classification (identification). Its main goal is to provide the reader with efficient tools for developing algorithms that solve his/her own image processing applications.
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Multivariate Exponential Families: A Concise Guide to Statistical Inference

Posted By: AvaxGenius
Date: 8 Oct 2021 05:02:04
Multivariate Exponential Families: A Concise Guide to Statistical Inference

Multivariate Exponential Families: A Concise Guide to Statistical Inference by Stefan Bedbur
English | PDF,EPUB | 2021 | 147 Pages | ISBN : 3030818993 | 10.8 MB

This book provides a concise introduction to exponential families. Parametric families of probability distributions and their properties are extensively studied in the literature on statistical modeling and inference. Exponential families of distributions comprise density functions of a particular form, which enables general assertions and leads to nice features.
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