Network Models Data Science by Izenman Alan (17 results)

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Hardcover. Condition: new. Hardcover. This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component. This book for graduate students in statistics, data science, computer science, machine learning, and mathematics explores the theory of complex networks, modern analysis methods, and computational issues. Applications range from technology and information to finance to social science to computational biology, physics, and engineering. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condition: Brand New. 484 pages. 10.00x7.00x1.25 inches. In Stock.

Hardcover. Condition: new. Hardcover. This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component. This book for graduate students in statistics, data science, computer science, machine learning, and mathematics explores the theory of complex networks, modern analysis methods, and computational issues. Applications range from technology and information to finance to social science to computational biology, physics, and engineering. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Gebunden. Condition: New. &Uumlber den AutorAlan J. Izenman is Professor of Statistical Science at Temple University. He received his Ph.D. from the University of California, Berkeley. He was a faculty member at Tel Aviv University and Colorado State University,.

Hardcover. Condition: Brand New. 484 pages. 10.00x7.00x1.25 inches. In Stock.

Hardcover. Condition: new. Hardcover. This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component. This book for graduate students in statistics, data science, computer science, machine learning, and mathematics explores the theory of complex networks, modern analysis methods, and computational issues. Applications range from technology and information to finance to social science to computational biology, physics, and engineering. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condition: Neu. Neuware - 'This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component'--.