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  • Combining, Modelling and Analyzing Imprecision, Randomness and Dependence

    Series series Springer Nature Proceedings excluding Computer Science
    This volume contains more than 65 peer-reviewed papers corresponding to presentations at the 11th Conference on Soft Methods in Probability and Statistics (SMPS) held in Salzburg, Austria, in September 2024. It covers recent advances in the field of probability, statistics, and data science, with a particular focus on dealing with dependence, imprecision and incomplete information. Reflecting the ... Read more

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    The field's leading text, now completely updated.Modeling dynamical systems — theory, methodology, and applications.Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and ... Read more

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  • Machine Learning

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    by Marco Gori ...
    Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic ... Read more

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  • Regularized System Identification

    Learning Dynamic Models from Data

    Series series Engineering (R0)
    This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The ... Read more

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  • Data Analysis

    Edited by Gérard Govaert ...
    The first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in ... Read more

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  • Probabilistic Machine Learning

    Advanced Topics

    Series series Adaptive Computation and Machine Learning series
    An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics ... Read more

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  • Introduction to Online Convex Optimization, second edition

    by Elad Hazan ...
    Series series Adaptive Computation and Machine Learning series
    New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization ... Read more

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  • Mathematical Statistics

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  • Handbook of Computational Statistics

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    Series series Mathematics and Statistics (R0)
    The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It ... Read more

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  • Learning Theory from First Principles

    by Francis Bach ...
    Series series Adaptive Computation and Machine Learning series
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  • Applied Reliability Engineering and Risk Analysis

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    Series series Quality and Reliability Engineering Series
    This complete resource on the theory and applications of reliability engineering, probabilistic models and risk analysis consolidates all the latest research, presenting the most up-to-date developments in this field.With comprehensive coverage of the theoretical and practical issues of both classic and modern topics, it also provides a unique commemoration to the centennial of the birth of Boris ... Read more

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  • Introduction to Functional Data Analysis

    Series series Chapman & Hall/CRC Texts in Statistical Science
    Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework.The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS ... Read more

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