Foundations of Statistical Algorithms
Foundations of Statistical Algorithms

Author: Claus Weihs

Publisher: CRC Press

Published: 2013-12-09

Total Pages: 495

ISBN-13: 1439878870

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A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today’s more powerful statistical algorithms. It emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the book reviews the upcoming challenge of scaling many of the established techniques to very large data sets and delves into systematic verification by demonstrating how to derive general classes of worst case inputs and emphasizing the importance of testing over a large number of different inputs. Broadly accessible, the book offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. After working through the material covered in the book, readers should not only understand current algorithms but also gain a deeper understanding of how algorithms are constructed, how to evaluate new algorithms, which recurring principles are used to tackle some of the tough problems statistical programmers face, and how to take an idea for a new method and turn it into something practically useful.

Fundamentals and Practice in Statistical Thermodynamics, Solutions Manual
Fundamentals and Practice in Statistical Thermodynamics, Solutions Manual

Author: Jianzhong Wu

Publisher: John Wiley & Sons

Published: 2024-08-20

Total Pages: 327

ISBN-13: 1394264097

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This is a solutions manual to accompany Fundamentals and Practice in Statistical Thermodynamics This textbook supplements, modernizes, and updates thermodynamics courses for both advanced undergraduates and graduate students by introducing the contemporary topics of statistical mechanics such as molecular simulation and liquid-state methods with a variety of realistic examples from the emerging areas of chemical and materials engineering. Current curriculum does not provide the necessary preparations required for a comprehensive understanding of these powerful tools for engineering applications. This text presents not only the fundamental ideas but also theoretical developments in molecular simulation and analytical methods to engineering students by illustrating why these topics are of pressing interest in modern high-tech applications.

Foundations of Statistical Algorithms
Foundations of Statistical Algorithms

Author: Claus Weihs

Publisher: CRC Press

Published: 2019-08-30

Total Pages: 474

ISBN-13: 9780367379094

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A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms. It emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the book reviews the upcoming challenge of scaling many of the established techniques to very large data sets and delves into systematic verification by demonstrating how to derive general classes of worst case inputs and emphasizing the importance of testing over a large number of different inputs. Broadly accessible, the book offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. After working through the material covered in the book, readers should not only understand current algorithms but also gain a deeper understanding of how algorithms are constructed, how to evaluate new algorithms, which recurring principles are used to tackle some of the tough problems statistical programmers face, and how to take an idea for a new method and turn it into something practically useful.

Foundations of Algorithms
Foundations of Algorithms

Author: Richard E. Neapolitan

Publisher: Jones & Bartlett Learning

Published: 2011

Total Pages: 647

ISBN-13: 0763782505

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Data Structures & Theory of Computation

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author: John D. Kelleher

Publisher: MIT Press

Published: 2020-10-20

Total Pages: 853

ISBN-13: 0262361108

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The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.